The principals of range resolution improvement the self-mixing ( autodyne ) surveillance radar for object detection on distances from ones to hundred meters are considered in this paper . The description of the flow chart , main parameters and results of test bed model examination are given . In this paper we show that multiple object tracking ( MOT ) can be formulated in a framework , where the detection and data-association are performed simultaneously . Our method allows us to overcome the confinements of data association based MOT approaches ; where the performance is dependent on the object detection results provided at input level . At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip . Abstract With the recent development of wearable cameras , the interest for research on the egocentric perspective is increasing . This opens the possibility to work on a specific object detection problem of hand detection and hand disambiguation . However , recent progress in egocentric hand disambiguation and even hand detection , especially using deep learning , has been limited by the lack of a large dataset , with suitable variations in subject , activity , and scene . In this paper , initially , a moving object is selected by frame differencing method and extracted the object by segment thresholding . The bubble sort algorithm ( BSA ) arranges the regions ( large to small ) to make sure that there is at least one big region ( object ) in object detection process . To track the object , a motion model is constructed to set the system models of Alpha-Beta ( α-β ) filter , Linear Kalman filter ( LKF ) and Extended Kalman filter ( EKF ) . The huge amount of laser scanned points are rapidly segmented as belonging either to the background ( i.e. fixed building ) or newly appeared objects by comparing them with the 3D virtual model . This segmentation result can not only accelerate the object detection process but also facilitate the further process of object recognition with significant reduction of redundant sensor data . Such a MR space can also help human operators realizing effective surveillance through real-time visualization of the object detection results . Object detecting and camera tracking in a large range of distances is still important problem in the research field of security and more recently also in the research field of assistive technologies too , when it is necessary to monitor intruders in the protected perimeter or elderly and disabled people both in hospital and at home . This paper discusses the design of an automatic stereoscopic video system using two identical pan-tilt-zoom ( PTZ ) cameras . A simple but effective algorithm of real-time servo visual tracking for a digital video recorder security system ( DVRSS ) is proposed . The DVRSS , which overcomes the drawbacks of a traditional analog video-based security system , provides functions of object detecting , security alarming , network communication , and real-time servo tracking . An object detecting algorithm based on adaptive thresholding to surmount the effect of light level variation is presented . Currently , the state-of-the-art method about object detector in image mainly depends on deep backbones , such as ResNet-50 , DarkNet-53 , ResNet-101 and DenseNet-169 , which benefits for their powerful capability of feature representations but suffers from high computational cost . On the basis of fast lightweight backbone network ( i.e. , VGG-16 ) , this paper improves the capability of feature representations by combining features of different receptive fields and cross-fusing feature pyramids , and finally establishes a fast and accurate detector . Multi - scale feature representations of CNNs are of great importance to a number of vision tasks including object detection , salient object detection , and semantic segmentation , boosting the model performance of those fields . subsubsection : Object detection . Effective CNN models need to locate objects of different scales in a scene . The architecture of our network inherits that of SSD , a recent object detection model . SSD proposed the idea of detecting objects on multiple feature layers with convolutional predictors . Our model also detects segments and links in a very similar way . CornerNet : Detecting Objects as Paired Keypoints section : The biases in the convolution layers that predict the attention maps are set according to . subsection : Detecting Objects CornerNet - Saccade uses the locations obtained from the downsized image to determine where to process . In this work , we define the quality of an hypothesis as its IoU with the ground truth , and the quality of the detector as the IoU threshold u used to train it . The goal is to investi - gate the , so far , poorly researched problem of learning high quality object detectors , whose outputs contain few close false positives , as shown in Figure 1 [ reference ] . Recently we participated in the restoration of the tower facades of the St. Martin dome in Rottenburg/Neckar , Germany . We combined laser scans , terrestrial imagery and images captured from a UAV platform , incorporating structure-from-motion , dense image matching , point cloud registration and production of orthographic projections , from which the CAD drawings could be derived . Stationary structured light scanning ( SLS ) serves as the reference method . As results , the surface models from dense image matching are very consistent with the reference . Their calculated roughness values accord to a high degree , both for 2D and 3D indices . Object detection is a famous branch of research in computer vision , many state of the art object detection algorithms have been introduced in the recent past , but how good are those object detectors when it comes to dense object detection ? In this paper we review common and highly accurate object detection methods on the scenes where numerous similar looking objects are placed in close proximity with each other . Object detection in remote sensing images is full of challenges . The large scale of detection region , and the difficulty of dense object detection are the main problems needed to solve . Although YOLOv3 perform better and faster , it is not suitable to the orientation which means it is not friendly to NMS operation on dense objects area . Given a particular color image , not all features from a color space , such as RGB , HSV , or Lab , are equally effective in describing the visual characteristics of segments . In this paper , we propose a projective clustering algorithm HCPC ( Hill-Climbing based Projective Clustering ) which utilizes EPCH ( an efficient projective clustering technique by histogram construction ) as the main framework and hill-climbing algorithm for dense region detection , for color image segmentation , thereby finding interesting clusters ( segments ) within subspaces of a given feature space . A new feature space , named HSVrVgVb , is also explored which is derived from HSV ( Hue , Saturation , and Value ) color space . We construct a set of special complex-valued integral images and an algorithm that allows to calculate Zernike moments fast , namely in constant time . The technique is suitable for dense detection procedures , where the image is scanned by a sliding window at multiple scales , and where rotational invariance is required at the level of each window . We assume no preliminary image segmentation . The proposed registration algorithm is used to match the moving background and remap video frames into a common co-ordinate system in order to stabilize that segment of video . The major modules include dense feature detection , sparse prominent edge and corner-based feature block identification , feature block region matching to extract control points , confidence weighted robust projective transformation estimation and image warping to register a segment of frames within a given temporal window . The proposed method is shown to produce good results with small image registration error . Based on the latest achievements in computer vision and RGB-D SLAM , a practical way for dense moving object segmentation and thus a new framework for robust dense RGB-D SLAM in challenging dynamic scenarios is put forward . As the state-of-the-art method in RGB-D SLAM , dense SLAM is very robust when there are motion blur or featureless regions , while most of those sparse feature-based methods could not handle them . The method avoids estimating the parameters of LFM and eliminates the different effects produced by Doppler Effect . Simulation illustrates that the proposed method can not only overcome the difficult in dense-moving target detection but also reduce 10dB detection threshold . We also integrate a dual attention module with our approach to increase the efficacy of the model in preferentially attending to the objects . We demonstrate that these additions help achieve significant improvements over state-of-the-art in dense object segmentation from user inputs , on multiple large-scale datasets . Through experiments , we demonstrate our method 's ability to generate high-quality training data as well as its scalability in incorporating extreme points , guiding clicks , and corrective clicks in a principled manner . To solve the problem , salient object existence prediction designed to judge whether the image contains salient objects is introduced into deep network to learn a better salient object detection model . For dense salient object detection task , high-level semantic feature is progressively hybrid upsampled from deep to shallow to remedy the spatial information loss guided by higher layer feature and saliency existence information . Our model is aware of non-salient image that contains no salient objects at all and thus reduces the false-positive rate . R101 improves the result 1.6 mAP over R50 . Dilated convolutions which is very successful in dense detection tasks increases accuracy 2 mAP over R50 architecture . However , dilated convolutional filters add more computational complexity , consequently hinder realtime performance . The main contributions of our work are within the CNN component which we describe first in the following . subsection : 3D CNNs for Dense Segmentation – Setting the Baseline CNNs produce estimates for the voxel - wise segmentation labels by classifying each voxel in an image independently taking the neighborhood , i.e. local and contextual image information , into account . The goal of this work is to present a systematic solution for RGB-D salient object detection , which addresses the following three aspects with a unified framework : modal-specific representation learning , complementary cue selection and cross-modal complement fusion . To learn discriminative modal-specific features , we propose a hierarchical cross-modal distillation scheme , in which the well-learned source modality provides supervisory signals to facilitate the learning process for the new modality . With the proposed saliency consensus process , we are able to generate an accurate saliency map based on these multiple predictions . Quantitative and qualitative evaluations on six challenging benchmark datasets against 18 competing algorithms demonstrate the effectiveness of our approach in learning the distribution of saliency maps , leading to a new state-of-the-art in RGB-D saliency detection . In this paper , we propose a two-stream refinement network for RGB-D saliency detection . A fusion refinement module is designed to fuse output features from different resolution and modals . Second , our CMAC framework further contains a fine-grained object part attention module to harness multiple discriminative object parts inside each possible object region for superior local feature representation . While greatly improving the accuracy of RGB-D object detection , the effective cross-modal information fusion as well as attentional context modeling in our proposed model provide an interpretable visualization scheme . Experimental results demonstrate that the proposed method significantly improves upon the state of the art on all public benchmarks . Image salient object detection ( SOD ) is an active research topic in computer vision and multimedia area . Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem . The main challenge for RGB-D salient object detection is how to exploit the salient cues of both intra-modality ( RGB , depth ) and cross-modality simultaneously which is known as cross-modality detection problem . In this paper , we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network ( \emph{cm}SalGAN ) . \emph{cm}SalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework , which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem . To further improve the detection results , the attention mechanism and edge detection module are also incorporated into \emph{cm}SalGAN . In this paper , we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network ( cmSalGAN ) . cmSalGAN aims to learn an optimal viewinvariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework , which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem . To further improve the detection results , the attention mechanism and edge detection module are also incorporated into cmSalGAN . The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology . This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection , demonstrating that combining color and depth information substantially improves quality of results . The proposed approach significantly outperforms the state-of-the-art methods on several publically available benchmark databases , demonstrating the superiority of Mr-CNN . We also apply our method to the RGB-D image saliency detection problem . Through learning saliency cues induced by depth and RGB information on pixel level jointly and their interactions , our model achieves better performance on predicting eye fixations in RGB-D images . RGB-induced salient object detection has recently witnessed substantial progress , which is attributed to the superior feature learning capability of deep convolutional neural networks ( CNNs ) . However , such detections suffer from challenging scenarios characterized by cluttered backgrounds , low-light conditions and variations in illumination . These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain . Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection . This paper aims to tackle the challenging problem of one-shot object detection . Given a query image patch whose class label is not included in the training data , the goal of the task is to detect all instances of the same class in a target image . The proposed approach learns to both extract transferable intra-class deformations , or " deltas " , between same-class pairs of training examples , and to apply those deltas to the few provided examples of a novel class ( unseen during training ) in order to efficiently synthesize samples from that new class . The proposed method improves over the state-of-the-art in one-shot object-recognition and compares favorably in the few-shot case . Upon acceptance code will be made available . We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner . We demonstrate the utility of our approach on the problems of ( 1 ) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes , as well as ( 2 ) object-based one-shot scene recognition . As external data , we leverage 3D depth and pose information from the SUN RGB-D dataset . Moreover , prior knowledge about each possible shot boundary such as its type and duration can be obtained in the pre-processing stage , which can accelerate the consequent hard cut and gradual transition detections . Experimental results indicate that the proposed framework is effective in accelerating the shot detection process , and it can also achieve excellent detection accuracies . This fusion is based on geometric properties of the scene and on the quality of each camera detection . Assessment of experiments using public datasets proposed for the evaluation of single camera object detection demonstrate the performance of our codebook based method for moving object detection in single view . Results using multi-camera open dataset also prove the efficiency of our multi-view detection approach . Most notably , by using feature space transfer for data augmentation ( w.r.t . pose and depth ) on SUN-RGBD objects , we demonstrate considerable performance improvements on one/few-shot object recognition in a transfer learning setup , compared to current state-of-the-art methods . Deep learning has revolutionized object detection thanks to large-scale datasets , but their object categories are still arguably very limited . In this paper , we attempt to enrich such categories by addressing the one-shot object detection problem , where the number of annotated training examples for learning an unseen class is limited to one . We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module , the combination of which integrates metric learning with an anchor-free Faster R-CNN-style detection pipeline , eventually eliminating the need to fine-tune on the support images . In this paper , we consider the problem of one-shot object segmentation in videos . Given an input video where the object mask of the first frame is provided , our goal is to segment each remaining frame in the video into foreground and background . Computational intelligence ( CI ) is awell-established paradigm that incorporates characteristics of biological computers ( brains ) to perform a variety of tasks that are difficult or impossible to do with conventional computers . This paper reviews some of the applications of CI in multimedia processing , including shot detection in video , logotype detection , video copy detection and retrieval , and faces coding in video sequences . The experimental results show that the proposed method has a recognition rate of 94.8 % , which is 2.7 % , 2.1 % and 2.8%higher than SSD300 , SSD321 and YOLOv2 , respectively . The detection rate is close to the method of Single-Shot Object Detection with Enriched Semantics ( DES ) , while still maintaining a reasonable detection speed of 27 FPS . In addition , since DSSD fuse the semantic information of each feature extraction layer , the proposed method also has good detection ability for small gesture objects .
The principals of range resolution improvement the self-mixing ( autodyne ) surveillance radar for object detection on distances from ones to hundred meters are considered in this paper . The description of the flow chart , main parameters and results of test bed model examination are given .
In this paper we show that multiple object tracking ( MOT ) can be formulated in a framework , where the detection and data-association are performed simultaneously . Our method allows us to overcome the confinements of data association based MOT approaches ; where the performance is dependent on the object detection results provided at input level . At the core of our method lies structured learning which learns a model for each target and infers the best location of all targets simultaneously in a video clip .
Abstract With the recent development of wearable cameras , the interest for research on the egocentric perspective is increasing . This opens the possibility to work on a specific object detection problem of hand detection and hand disambiguation . However , recent progress in egocentric hand disambiguation and even hand detection , especially using deep learning , has been limited by the lack of a large dataset , with suitable variations in subject , activity , and scene .
In this paper , initially , a moving object is selected by frame differencing method and extracted the object by segment thresholding . The bubble sort algorithm ( BSA ) arranges the regions ( large to small ) to make sure that there is at least one big region ( object ) in object detection process . To track the object , a motion model is constructed to set the system models of Alpha-Beta ( α-β ) filter , Linear Kalman filter ( LKF ) and Extended Kalman filter ( EKF ) .
The huge amount of laser scanned points are rapidly segmented as belonging either to the background ( i.e. fixed building ) or newly appeared objects by comparing them with the 3D virtual model . This segmentation result can not only accelerate the object detection process but also facilitate the further process of object recognition with significant reduction of redundant sensor data . Such a MR space can also help human operators realizing effective surveillance through real-time visualization of the object detection results .
Object detecting and camera tracking in a large range of distances is still important problem in the research field of security and more recently also in the research field of assistive technologies too , when it is necessary to monitor intruders in the protected perimeter or elderly and disabled people both in hospital and at home . This paper discusses the design of an automatic stereoscopic video system using two identical pan-tilt-zoom ( PTZ ) cameras .
A simple but effective algorithm of real-time servo visual tracking for a digital video recorder security system ( DVRSS ) is proposed . The DVRSS , which overcomes the drawbacks of a traditional analog video-based security system , provides functions of object detecting , security alarming , network communication , and real-time servo tracking . An object detecting algorithm based on adaptive thresholding to surmount the effect of light level variation is presented .
Currently , the state-of-the-art method about object detector in image mainly depends on deep backbones , such as ResNet-50 , DarkNet-53 , ResNet-101 and DenseNet-169 , which benefits for their powerful capability of feature representations but suffers from high computational cost . On the basis of fast lightweight backbone network ( i.e. , VGG-16 ) , this paper improves the capability of feature representations by combining features of different receptive fields and cross-fusing feature pyramids , and finally establishes a fast and accurate detector .
Multi - scale feature representations of CNNs are of great importance to a number of vision tasks including object detection , salient object detection , and semantic segmentation , boosting the model performance of those fields . subsubsection : Object detection . Effective CNN models need to locate objects of different scales in a scene .
The architecture of our network inherits that of SSD , a recent object detection model . SSD proposed the idea of detecting objects on multiple feature layers with convolutional predictors . Our model also detects segments and links in a very similar way .
CornerNet : Detecting Objects as Paired Keypoints section :
The biases in the convolution layers that predict the attention maps are set according to . subsection : Detecting Objects CornerNet - Saccade uses the locations obtained from the downsized image to determine where to process .
In this work , we define the quality of an hypothesis as its IoU with the ground truth , and the quality of the detector as the IoU threshold u used to train it . The goal is to investi - gate the , so far , poorly researched problem of learning high quality object detectors , whose outputs contain few close false positives , as shown in Figure 1 [ reference ] .
Recently we participated in the restoration of the tower facades of the St. Martin dome in Rottenburg/Neckar , Germany . We combined laser scans , terrestrial imagery and images captured from a UAV platform , incorporating structure-from-motion , dense image matching , point cloud registration and production of orthographic projections , from which the CAD drawings could be derived .
Stationary structured light scanning ( SLS ) serves as the reference method . As results , the surface models from dense image matching are very consistent with the reference . Their calculated roughness values accord to a high degree , both for 2D and 3D indices .
Object detection is a famous branch of research in computer vision , many state of the art object detection algorithms have been introduced in the recent past , but how good are those object detectors when it comes to dense object detection ? In this paper we review common and highly accurate object detection methods on the scenes where numerous similar looking objects are placed in close proximity with each other .
Object detection in remote sensing images is full of challenges . The large scale of detection region , and the difficulty of dense object detection are the main problems needed to solve . Although YOLOv3 perform better and faster , it is not suitable to the orientation which means it is not friendly to NMS operation on dense objects area .
Given a particular color image , not all features from a color space , such as RGB , HSV , or Lab , are equally effective in describing the visual characteristics of segments . In this paper , we propose a projective clustering algorithm HCPC ( Hill-Climbing based Projective Clustering ) which utilizes EPCH ( an efficient projective clustering technique by histogram construction ) as the main framework and hill-climbing algorithm for dense region detection , for color image segmentation , thereby finding interesting clusters ( segments ) within subspaces of a given feature space . A new feature space , named HSVrVgVb , is also explored which is derived from HSV ( Hue , Saturation , and Value ) color space .
We construct a set of special complex-valued integral images and an algorithm that allows to calculate Zernike moments fast , namely in constant time . The technique is suitable for dense detection procedures , where the image is scanned by a sliding window at multiple scales , and where rotational invariance is required at the level of each window . We assume no preliminary image segmentation .
The proposed registration algorithm is used to match the moving background and remap video frames into a common co-ordinate system in order to stabilize that segment of video . The major modules include dense feature detection , sparse prominent edge and corner-based feature block identification , feature block region matching to extract control points , confidence weighted robust projective transformation estimation and image warping to register a segment of frames within a given temporal window . The proposed method is shown to produce good results with small image registration error .
Based on the latest achievements in computer vision and RGB-D SLAM , a practical way for dense moving object segmentation and thus a new framework for robust dense RGB-D SLAM in challenging dynamic scenarios is put forward . As the state-of-the-art method in RGB-D SLAM , dense SLAM is very robust when there are motion blur or featureless regions , while most of those sparse feature-based methods could not handle them .
The method avoids estimating the parameters of LFM and eliminates the different effects produced by Doppler Effect . Simulation illustrates that the proposed method can not only overcome the difficult in dense-moving target detection but also reduce 10dB detection threshold .
We also integrate a dual attention module with our approach to increase the efficacy of the model in preferentially attending to the objects . We demonstrate that these additions help achieve significant improvements over state-of-the-art in dense object segmentation from user inputs , on multiple large-scale datasets . Through experiments , we demonstrate our method 's ability to generate high-quality training data as well as its scalability in incorporating extreme points , guiding clicks , and corrective clicks in a principled manner .
To solve the problem , salient object existence prediction designed to judge whether the image contains salient objects is introduced into deep network to learn a better salient object detection model . For dense salient object detection task , high-level semantic feature is progressively hybrid upsampled from deep to shallow to remedy the spatial information loss guided by higher layer feature and saliency existence information . Our model is aware of non-salient image that contains no salient objects at all and thus reduces the false-positive rate .
R101 improves the result 1.6 mAP over R50 . Dilated convolutions which is very successful in dense detection tasks increases accuracy 2 mAP over R50 architecture . However , dilated convolutional filters add more computational complexity , consequently hinder realtime performance .
The main contributions of our work are within the CNN component which we describe first in the following . subsection : 3D CNNs for Dense Segmentation – Setting the Baseline CNNs produce estimates for the voxel - wise segmentation labels by classifying each voxel in an image independently taking the neighborhood , i.e. local and contextual image information , into account .
The goal of this work is to present a systematic solution for RGB-D salient object detection , which addresses the following three aspects with a unified framework : modal-specific representation learning , complementary cue selection and cross-modal complement fusion . To learn discriminative modal-specific features , we propose a hierarchical cross-modal distillation scheme , in which the well-learned source modality provides supervisory signals to facilitate the learning process for the new modality .
With the proposed saliency consensus process , we are able to generate an accurate saliency map based on these multiple predictions . Quantitative and qualitative evaluations on six challenging benchmark datasets against 18 competing algorithms demonstrate the effectiveness of our approach in learning the distribution of saliency maps , leading to a new state-of-the-art in RGB-D saliency detection .
In this paper , we propose a two-stream refinement network for RGB-D saliency detection . A fusion refinement module is designed to fuse output features from different resolution and modals .
Second , our CMAC framework further contains a fine-grained object part attention module to harness multiple discriminative object parts inside each possible object region for superior local feature representation . While greatly improving the accuracy of RGB-D object detection , the effective cross-modal information fusion as well as attentional context modeling in our proposed model provide an interpretable visualization scheme . Experimental results demonstrate that the proposed method significantly improves upon the state of the art on all public benchmarks .
Image salient object detection ( SOD ) is an active research topic in computer vision and multimedia area . Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem . The main challenge for RGB-D salient object detection is how to exploit the salient cues of both intra-modality ( RGB , depth ) and cross-modality simultaneously which is known as cross-modality detection problem . In this paper , we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network ( \emph{cm}SalGAN ) . \emph{cm}SalGAN aims to learn an optimal view-invariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework , which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem . To further improve the detection results , the attention mechanism and edge detection module are also incorporated into \emph{cm}SalGAN .
In this paper , we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network ( cmSalGAN ) . cmSalGAN aims to learn an optimal viewinvariant and consistent pixel-level representation for RGB and depth images via a novel adversarial learning framework , which thus incorporates both information of intra-view and correlation information of cross-view images simultaneously for RGB-D saliency detection problem . To further improve the detection results , the attention mechanism and edge detection module are also incorporated into cmSalGAN .
The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology . This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection , demonstrating that combining color and depth information substantially improves quality of results .
The proposed approach significantly outperforms the state-of-the-art methods on several publically available benchmark databases , demonstrating the superiority of Mr-CNN . We also apply our method to the RGB-D image saliency detection problem . Through learning saliency cues induced by depth and RGB information on pixel level jointly and their interactions , our model achieves better performance on predicting eye fixations in RGB-D images .
RGB-induced salient object detection has recently witnessed substantial progress , which is attributed to the superior feature learning capability of deep convolutional neural networks ( CNNs ) . However , such detections suffer from challenging scenarios characterized by cluttered backgrounds , low-light conditions and variations in illumination .
These two schemes use different information to construct the relationship and transfer knowledge from the source-domain to the target-domain . Experiments on the PASCAL-OSCD benchmark show that our methods can significantly boost the performance of one-shot object detection .
This paper aims to tackle the challenging problem of one-shot object detection . Given a query image patch whose class label is not included in the training data , the goal of the task is to detect all instances of the same class in a target image .
The proposed approach learns to both extract transferable intra-class deformations , or " deltas " , between same-class pairs of training examples , and to apply those deltas to the few provided examples of a novel class ( unseen during training ) in order to efficiently synthesize samples from that new class . The proposed method improves over the state-of-the-art in one-shot object-recognition and compares favorably in the few-shot case . Upon acceptance code will be made available .
We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner . We demonstrate the utility of our approach on the problems of ( 1 ) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes , as well as ( 2 ) object-based one-shot scene recognition . As external data , we leverage 3D depth and pose information from the SUN RGB-D dataset .
Moreover , prior knowledge about each possible shot boundary such as its type and duration can be obtained in the pre-processing stage , which can accelerate the consequent hard cut and gradual transition detections . Experimental results indicate that the proposed framework is effective in accelerating the shot detection process , and it can also achieve excellent detection accuracies .
This fusion is based on geometric properties of the scene and on the quality of each camera detection . Assessment of experiments using public datasets proposed for the evaluation of single camera object detection demonstrate the performance of our codebook based method for moving object detection in single view . Results using multi-camera open dataset also prove the efficiency of our multi-view detection approach .
Most notably , by using feature space transfer for data augmentation ( w.r.t . pose and depth ) on SUN-RGBD objects , we demonstrate considerable performance improvements on one/few-shot object recognition in a transfer learning setup , compared to current state-of-the-art methods .
Deep learning has revolutionized object detection thanks to large-scale datasets , but their object categories are still arguably very limited . In this paper , we attempt to enrich such categories by addressing the one-shot object detection problem , where the number of annotated training examples for learning an unseen class is limited to one . We introduce a two-stage model consisting of a first stage Matching-FCOS network and a second stage Structure-Aware Relation Module , the combination of which integrates metric learning with an anchor-free Faster R-CNN-style detection pipeline , eventually eliminating the need to fine-tune on the support images .
In this paper , we consider the problem of one-shot object segmentation in videos . Given an input video where the object mask of the first frame is provided , our goal is to segment each remaining frame in the video into foreground and background .
Computational intelligence ( CI ) is awell-established paradigm that incorporates characteristics of biological computers ( brains ) to perform a variety of tasks that are difficult or impossible to do with conventional computers . This paper reviews some of the applications of CI in multimedia processing , including shot detection in video , logotype detection , video copy detection and retrieval , and faces coding in video sequences .
The experimental results show that the proposed method has a recognition rate of 94.8 % , which is 2.7 % , 2.1 % and 2.8%higher than SSD300 , SSD321 and YOLOv2 , respectively . The detection rate is close to the method of Single-Shot Object Detection with Enriched Semantics ( DES ) , while still maintaining a reasonable detection speed of 27 FPS . In addition , since DSSD fuse the semantic information of each feature extraction layer , the proposed method also has good detection ability for small gesture objects .
Range resolution improvement for low-cost FMCW self-mixing radar Target Identity-aware Network Flow for online multiple target tracking Is that my hand? An egocentric dataset for hand disambiguation A New Method for Ball Tracking Based on α-β, Linear Kalman and Extended Kalman Filters Via Bubble Sort Algorithm Object Detection for a Mobile Robot Using Mixed Reality Cost-effective automatic stereoscopic security video system 3DSec Real-Time Servo Visual Tracking of Digital Video Recorder Security Systems Feature Combination Based on Receptive Fields and Cross-Fusion Feature Pyramid for Object Detection Res2Net: A New Multi-scale Backbone Architecture Detecting Oriented Text in Natural Images by Linking Segments CornerNet: Detecting Objects as Paired Keypoints CornerNet-Lite: Efficient Keypoint Based Object Detection Cascade R-CNN: Delving Into High Quality Object Detection Image based 3D Reconstruction in Cultural Heritage Preservation On the usability of different optical measuring techniques for joint roughness evaluation Learning Gaussian Maps for Dense Object Detection Orientation Adaptive YOLOv3 for Object Detection in Remote Sensing Images An improvement of color image segmentation through projective clustering Constant-Time Calculation of Zernike Moments for Detection with Rotational Invariance UAV-Video Registration Using Block-Based Features Towards dense moving object segmentation based robust dense RGB-D SLAM in dynamic scenarios DENSE-MOVING TARGET DETECTION OF LFMCW RADAR FAIRS - Soft Focus Generator and Attention for Robust Object Segmentation from Extreme Points Robust salient object detection for RGB images MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network Efficient multi‐scale 3D CNN with fully connected CRF for accurate brain lesion segmentation CNN-based RGB-D Salient Object Detection: Learn, Select and Fuse UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders Two-Stream Refinement Network for RGB-D Saliency Detection Cross-Modal Attentional Context Learning for RGB-D Object Detection \emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks. cmSalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks A large-scale hierarchical multi-view RGB-D object dataset Learning to Predict Eye Fixations via Multiresolution Convolutional Neural Networks RGB-T Salient Object Detection via Fusing Multi-Level CNN Features Network Parameter Generation for One-Shot Object Detection One-Shot Object Detection with Co-Attention and Co-Excitation Delta-encoder: an effective sample synthesis method for few-shot object recognition AGA: Attribute-Guided Augmentation Fast video shot boundary detection framework employing pre-processing techniques Fast moving object detection from overlapping cameras Feature Space Transfer for Data Augmentation One-Shot Object Detection without Fine-Tuning One-Shot Video Object Segmentation Using Attention Transfer Computational Intelligence in Multimedia Processing A real-time recognition method of static gesture based on DSSD
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CVPR VSMM ICCST ICONIP (2) IEEE transactions on pattern analysis and machine intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ICINCO (1) PRCV (1) IRI IGARSS (2) ICARCV ECCV Medical Image Anal. ICIP ICRA ICINCO (2) MMSP IWANN (1)
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