Visual object tracking is an active research field in the area of computer vision . The tracking process usually includes the construction of an object appearance model and the object localization . This paper investigates the use of Particle Swarm Optimization ( PSO ) as the object localization method based on the Bayesian tracking framework . However , because object classification algorithms tend to be computationally expensive , it is helpful to have an effective filter to reduce the number of times those classifiers have to be run . In this thesis I evaluate one promising approach to object localization : the objectness algorithm proposed in [ 1 , 2 ] . Specifically , I verify the results given in [ 1 , 2 ] and further explore the weaknesses and strengths of their “ objectness ” approach . Our goal is to improve performance by incorporating privileged information into ordinary learning framework and adjusting model parameters for better generalization . We tackle object localization problem based on a novel structural SVM using privileged information , where an alternating loss-augmented inference procedure is employed to handle the term in the objective function corresponding to privileged information . We apply the proposed algorithm to the Caltech-UCSD Birds 200 - 2011 dataset , and obtain encouraging results suggesting further investigation into the benefit of privileged information in structured prediction . The existing spatial localization methods without the aid of lidar and other equipment often have problems in restoring the real scale . In order to overcome this problem and achieve more accurate object spatial localization , an object spatial localization by fusing 3D point clouds and instance segmentation is proposed . This method obtains sparse 3D point cloud data by binocular stereo matching , which is used to describe the real scale and spatial location information of the object . section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks . Hinton et al . have also proposed to train networks to compute explicit instantiation parameters of features as part of a recognition process . Other authors have proposed to perform object localization via ConvNet - based segmentation . The simplest approach consists in training the ConvNet to classify the central pixel ( or voxel for volumetric images ) of its viewing window as a boundary between regions or not . Our method also generalizes well across domains and to unseen object categories . Without fine-tuning for the specific tasks , our model trained on VOC12 dataset ( 20 classes ) obtains top performance for weakly supervised object localization on the CUB dataset ( 200 classes ) and achieves competitive results on three widely used salient object detection benchmarks . Optimizing smoothed loss functions prevents the training procedure falling prematurely into local minima and facilitates the discovery of Stable Semantic Extremal Regions ( SSERs ) which indicate full object extent . On the PASCAL VOC 2007 and 2012 datasets , C-MIL improves the state-of-the-art of weakly supervised object detection and weakly supervised object localization with large margins . Weakly-supervised object detection has recently attracted increasing attention since it only requires image-level annotations . However , the performance obtained by existing methods is still far from being satisfactory compared with fully-supervised object detection methods . It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of convolutional networks . We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset . We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps . In experiments , we visualize the evidence of a model ’s classification decision by computing the proposed top-down attention maps . For quantitative evaluation , we report the accuracy of our method in weakly supervised localization tasks on the MS COCO , PASCAL VOC07 and ImageNet datasets . The usefulness of our method is further validated in the text-to-region association task . And we provide a set of evaluation metrics and use these metrics to compare and evaluate the recall situation of these object proposal methods . Using the ground-truth instance for exploring unsupervised object localization on PASCAL VOC 2007 , MSRA10 K and Object Discovery datasets . Our experimental analysis demonstrates that the performance of these methods depends on the number of candidates and the threshold of recall , meanwhile the methods that generate the candidates with scores perform better than those methods that do not with scores . High order features have been proposed to incorporate geometrical information into the " bag of feature " representation . We propose algorithms to perform fast weakly supervised object categorization and localization with high order features . To this end , we first use Hough transform method to identify translation and scale invariant high order features co-occurring in two images . In the SP-augmented CNNs , referred to as Soft Proposal Networks ( SPNs ) , iteratively evolved object proposals are generated based on the deep feature maps then projected back , and further jointly optimized with network parameters , with image-level supervision only . Through the unified learning process , SPNs learn better object-centric filters , discover more discriminative visual evidence , and suppress background interference , significantly boosting both weakly supervised object localization and classification performance . We report the best results on popular benchmarks , including PASCAL VOC , MS COCO , and ImageNet . Furthermore , a novel constrain-to-highlight loss is proposed to balances region selection among hierarchical feature maps , which additional improve model performance . Extensive experiments on object classification and weakly supervised pointwise object localization show that WALLACE achieves state-of-the-art results on the VOC 2007 and VOC 2012 benchmark without bells and whistles . section : Abstract Weakly - supervised object detection ( WOD ) is a challenging problems in computer vision . The key problem is to simultaneously infer the exact object locations in the training images and train the object detectors , given only the training images with weak image - level labels . document : Multiple Instance Detection Network with Online Instance Classifier Refinement Of late , weakly supervised object detection is with great importance in object recognition . Based on deep learning , weakly supervised detectors have achieved many promising results . Addressing this issue will therefore require incorporating additional cue in the model to try to learn the " whole object " . The output of our model could also be used as input to one of the existing methods for weakly - supervised detection that use a CNN as a black - box for feature extraction . Investigating this option is left to future work . In passive localization , sensors try to locate an event without any knowledge of event 's emitted power . So , this is a more challenging problem compared to active localization . Existing passive localization schemes use expensive and noise-vulnerable range-based techniques . The appropriateness of our approach is demonstrated empirically in both simulation & real-time ( on Amigo-bots ) and its efficacy verified . Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense . It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither . By implementing the algorithm on a PC cluster system , we have succeeded in real-time 3D shape reconstruction . Then to get the 3D shape of a moving object in a wide space , we introduce active object tracking by multiple cameras . To realize the active 3D shape reconstruction , we augmented the volume intersection method so that it can be applied to those images captured by active cameras . We study active object tracking , where a tracker takes visual observations ( i.e. , frame sequences ) as input and produces the corresponding camera control signals as output ( e.g. , move forward , turn left , etc . ) . Conventional methods tackle tracking and camera control tasks separately , and the resulting system is difficult to tune jointly . Active object detection refers to the problem of determining the existence and location of objects in an image by actively selecting which regions of the image to explore . Herein , an object detection algorithm is proposed that models image regions as vertices and overlap relationships as edges in a directed weighted graph . For mobile robots to localize robustly , actively considering the perception requirement at the planning stage is essential . In this paper , we propose a novel representation for active visual localization . By formulating the Fisher information and sensor visibility carefully , we are able to summarize the localization information into a discrete grid , namely the Fisher information field . An efficient noniterative algorithm for active localization of objects is developed . It is based on intersecting elliptic curves defined by uncertain range-sum measurements between a signal source , the objects , and a number of arbitrarily located receivers . We describe a method for performing active localization of objects in instances of visual situations . A visual situation is an abstract concept --- e.g . , " a boxing match " , " a birthday party " , " walking the dog " , " waiting for a bus "--- whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity . Sensor systems of varying ability have been proposed and successfully used to solve the problem . This paper probes the lower limits of this range by describing three extremely simple robot models and addresses the active localization problem for each . The robot , whose configuration is composed of its position and orientation , moves in a fully-known , simply connected polygonal environment . This paper presents a probabilistic method for active localization of needles and targets in robotic image guided interventions . Specifically , an active localization scenario where the system directly controls the imaging system to actively localize the needle and target locations using intra-operative medical imaging ( e.g. , computerized tomography and ultrasound imaging ) is explored . In the proposed method , the active localization problem is posed as an information maximization problem , where the beliefs for the needle and target states are represented and estimated using particle filters . The proposed method is also validated using a simulation study . Two versions are considered : a static Tac Whisker array analogous to immotile tactile vibrissae ( e.g. rodent microvib-rissae ) and a dynamic Tac Whisker array analogous to motile tactile vibrissae ( e.g. rodent macrovibrissae ) . Performance is assessed on an active object localization task . The whisking motion of the dynamic Tac Whisker leads to millimetre-scale location perception , whereas perception with the static Tac Whisker array is relatively poor when making dabbing contacts . A major goal of computer vision is to enable computers to interpret visual situations — abstract concepts ( e.g. , “ a person walking a dog , ” “ a crowd waiting for a bus , ” “ a picnic ” ) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity . In this paper , we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images . In our system , prior situation knowledge is captured by a set of flexible , kernel-based density estimations — a situation model — that represent the expected spatial structure of the given situation . Further , we demonstrate effectiveness of the approach for multiple applications . First , we show results for active object viewpoint localization to capture images from similar pose considering only a single image as pose reference . Second , we demonstrate the ability of the learned semantic correspondence to perform unsupervised part-segmentation transfer using only a single part-annotated 3D template model per object class . This paper describes a method to localize a mobile robot in a structured indoor environment from visual information provided by omnidirectional images . In particular , an approximate algorithm for active robot localization in partially observable environments based on a POMDP model is introduced . Results from many different tests are presented to prove the setting of algorithm parameters .
Visual object tracking is an active research field in the area of computer vision . The tracking process usually includes the construction of an object appearance model and the object localization . This paper investigates the use of Particle Swarm Optimization ( PSO ) as the object localization method based on the Bayesian tracking framework .
However , because object classification algorithms tend to be computationally expensive , it is helpful to have an effective filter to reduce the number of times those classifiers have to be run . In this thesis I evaluate one promising approach to object localization : the objectness algorithm proposed in [ 1 , 2 ] . Specifically , I verify the results given in [ 1 , 2 ] and further explore the weaknesses and strengths of their “ objectness ” approach .
Our goal is to improve performance by incorporating privileged information into ordinary learning framework and adjusting model parameters for better generalization . We tackle object localization problem based on a novel structural SVM using privileged information , where an alternating loss-augmented inference procedure is employed to handle the term in the objective function corresponding to privileged information . We apply the proposed algorithm to the Caltech-UCSD Birds 200 - 2011 dataset , and obtain encouraging results suggesting further investigation into the benefit of privileged information in structured prediction .
The existing spatial localization methods without the aid of lidar and other equipment often have problems in restoring the real scale . In order to overcome this problem and achieve more accurate object spatial localization , an object spatial localization by fusing 3D point clouds and instance segmentation is proposed . This method obtains sparse 3D point cloud data by binocular stereo matching , which is used to describe the real scale and spatial location information of the object .
section : Introduction Since the 2012 ImageNet competition winning entry by Krizhevsky et al , their network “ AlexNet ” has been successfully applied to a larger variety of computer vision tasks , for example to object - detection , segmentation , human pose estimation , video classification , object tracking , and superresolution . These successes spurred a new line of research that focused on finding higher performing convolutional neural networks .
Hinton et al . have also proposed to train networks to compute explicit instantiation parameters of features as part of a recognition process . Other authors have proposed to perform object localization via ConvNet - based segmentation . The simplest approach consists in training the ConvNet to classify the central pixel ( or voxel for volumetric images ) of its viewing window as a boundary between regions or not .
Our method also generalizes well across domains and to unseen object categories . Without fine-tuning for the specific tasks , our model trained on VOC12 dataset ( 20 classes ) obtains top performance for weakly supervised object localization on the CUB dataset ( 200 classes ) and achieves competitive results on three widely used salient object detection benchmarks .
Optimizing smoothed loss functions prevents the training procedure falling prematurely into local minima and facilitates the discovery of Stable Semantic Extremal Regions ( SSERs ) which indicate full object extent . On the PASCAL VOC 2007 and 2012 datasets , C-MIL improves the state-of-the-art of weakly supervised object detection and weakly supervised object localization with large margins .
Weakly-supervised object detection has recently attracted increasing attention since it only requires image-level annotations . However , the performance obtained by existing methods is still far from being satisfactory compared with fully-supervised object detection methods .
It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of convolutional networks . We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset . We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps .
In experiments , we visualize the evidence of a model ’s classification decision by computing the proposed top-down attention maps . For quantitative evaluation , we report the accuracy of our method in weakly supervised localization tasks on the MS COCO , PASCAL VOC07 and ImageNet datasets . The usefulness of our method is further validated in the text-to-region association task .
And we provide a set of evaluation metrics and use these metrics to compare and evaluate the recall situation of these object proposal methods . Using the ground-truth instance for exploring unsupervised object localization on PASCAL VOC 2007 , MSRA10 K and Object Discovery datasets . Our experimental analysis demonstrates that the performance of these methods depends on the number of candidates and the threshold of recall , meanwhile the methods that generate the candidates with scores perform better than those methods that do not with scores .
High order features have been proposed to incorporate geometrical information into the " bag of feature " representation . We propose algorithms to perform fast weakly supervised object categorization and localization with high order features . To this end , we first use Hough transform method to identify translation and scale invariant high order features co-occurring in two images .
In the SP-augmented CNNs , referred to as Soft Proposal Networks ( SPNs ) , iteratively evolved object proposals are generated based on the deep feature maps then projected back , and further jointly optimized with network parameters , with image-level supervision only . Through the unified learning process , SPNs learn better object-centric filters , discover more discriminative visual evidence , and suppress background interference , significantly boosting both weakly supervised object localization and classification performance . We report the best results on popular benchmarks , including PASCAL VOC , MS COCO , and ImageNet .
Furthermore , a novel constrain-to-highlight loss is proposed to balances region selection among hierarchical feature maps , which additional improve model performance . Extensive experiments on object classification and weakly supervised pointwise object localization show that WALLACE achieves state-of-the-art results on the VOC 2007 and VOC 2012 benchmark without bells and whistles .
section : Abstract Weakly - supervised object detection ( WOD ) is a challenging problems in computer vision . The key problem is to simultaneously infer the exact object locations in the training images and train the object detectors , given only the training images with weak image - level labels .
document : Multiple Instance Detection Network with Online Instance Classifier Refinement Of late , weakly supervised object detection is with great importance in object recognition . Based on deep learning , weakly supervised detectors have achieved many promising results .
Addressing this issue will therefore require incorporating additional cue in the model to try to learn the " whole object " . The output of our model could also be used as input to one of the existing methods for weakly - supervised detection that use a CNN as a black - box for feature extraction . Investigating this option is left to future work .
In passive localization , sensors try to locate an event without any knowledge of event 's emitted power . So , this is a more challenging problem compared to active localization . Existing passive localization schemes use expensive and noise-vulnerable range-based techniques .
The appropriateness of our approach is demonstrated empirically in both simulation & real-time ( on Amigo-bots ) and its efficacy verified . Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense . It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither .
By implementing the algorithm on a PC cluster system , we have succeeded in real-time 3D shape reconstruction . Then to get the 3D shape of a moving object in a wide space , we introduce active object tracking by multiple cameras . To realize the active 3D shape reconstruction , we augmented the volume intersection method so that it can be applied to those images captured by active cameras .
We study active object tracking , where a tracker takes visual observations ( i.e. , frame sequences ) as input and produces the corresponding camera control signals as output ( e.g. , move forward , turn left , etc . ) . Conventional methods tackle tracking and camera control tasks separately , and the resulting system is difficult to tune jointly .
Active object detection refers to the problem of determining the existence and location of objects in an image by actively selecting which regions of the image to explore . Herein , an object detection algorithm is proposed that models image regions as vertices and overlap relationships as edges in a directed weighted graph .
For mobile robots to localize robustly , actively considering the perception requirement at the planning stage is essential . In this paper , we propose a novel representation for active visual localization . By formulating the Fisher information and sensor visibility carefully , we are able to summarize the localization information into a discrete grid , namely the Fisher information field .
An efficient noniterative algorithm for active localization of objects is developed . It is based on intersecting elliptic curves defined by uncertain range-sum measurements between a signal source , the objects , and a number of arbitrarily located receivers .
We describe a method for performing active localization of objects in instances of visual situations . A visual situation is an abstract concept --- e.g . , " a boxing match " , " a birthday party " , " walking the dog " , " waiting for a bus "--- whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity .
Sensor systems of varying ability have been proposed and successfully used to solve the problem . This paper probes the lower limits of this range by describing three extremely simple robot models and addresses the active localization problem for each . The robot , whose configuration is composed of its position and orientation , moves in a fully-known , simply connected polygonal environment .
This paper presents a probabilistic method for active localization of needles and targets in robotic image guided interventions . Specifically , an active localization scenario where the system directly controls the imaging system to actively localize the needle and target locations using intra-operative medical imaging ( e.g. , computerized tomography and ultrasound imaging ) is explored . In the proposed method , the active localization problem is posed as an information maximization problem , where the beliefs for the needle and target states are represented and estimated using particle filters . The proposed method is also validated using a simulation study .
Two versions are considered : a static Tac Whisker array analogous to immotile tactile vibrissae ( e.g. rodent microvib-rissae ) and a dynamic Tac Whisker array analogous to motile tactile vibrissae ( e.g. rodent macrovibrissae ) . Performance is assessed on an active object localization task . The whisking motion of the dynamic Tac Whisker leads to millimetre-scale location perception , whereas perception with the static Tac Whisker array is relatively poor when making dabbing contacts .
A major goal of computer vision is to enable computers to interpret visual situations — abstract concepts ( e.g. , “ a person walking a dog , ” “ a crowd waiting for a bus , ” “ a picnic ” ) whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity . In this paper , we propose a novel method for prior learning and active object localization for this kind of knowledge-driven search in static images . In our system , prior situation knowledge is captured by a set of flexible , kernel-based density estimations — a situation model — that represent the expected spatial structure of the given situation .
Further , we demonstrate effectiveness of the approach for multiple applications . First , we show results for active object viewpoint localization to capture images from similar pose considering only a single image as pose reference . Second , we demonstrate the ability of the learned semantic correspondence to perform unsupervised part-segmentation transfer using only a single part-annotated 3D template model per object class .
This paper describes a method to localize a mobile robot in a structured indoor environment from visual information provided by omnidirectional images . In particular , an approximate algorithm for active robot localization in partially observable environments based on a POMDP model is introduced . Results from many different tests are presented to prove the setting of algorithm parameters .
Object tracking using Particle Swarm Optimization and Earth mover's distance Investigations of an "Objectness" Measure for Object Localization Object Localization based on Structural SVM using Privileged Information Object spatial localization by fusing 3D point clouds and instance segmentation Rethinking the Inception Architecture for Computer Vision OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Learning Instance Activation Maps for Weakly Supervised Instance Segmentation C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection Learning Object Scale With Click Supervision for Object Detection STNet: Selective Tuning of Convolutional Networks for Object Localization Top-Down Neural Attention by Excitation Backprop Object Proposal Generation for Unsupervised Object Localization Weakly Supervised Object Recognition and Localization with Invariant High Order Features Soft Proposal Networks for Weakly Supervised Object Localization WALLACE: Weakly Supervised Learning of Deep Convolutional Neural Networks With Multiscale Evidence Bridging Saliency Detection to Weakly Supervised Object Detection Based on Self-Paced Curriculum Learning Multiple Instance Detection Network with Online Instance Classifier Refinement Weakly Supervised Deep Detection Networks Range-free passive localization using static and mobile sensors Motion in ambiguity: Coordinated active global localization for multiple robots Real-time active 3D shape reconstruction for 3D video End-to-End Active Object Tracking and Its Real-World Deployment via Reinforcement Learning Active object detection on graphs via locally informative trees Beyond Point Clouds: Fisher Information Field for Active Visual Localization Closed-form elliptic location with an arbitrary array topology Active Object Localization in Visual Situations Localization With Limited Sensing Particle filter based active localization of target and needle in robotic image-guided intervention systems TacWhiskers: Biomimetic Optical Tactile Whiskered Robots Fast on-line kernel density estimation for active object localization iSPA-Net: Iterative Semantic Pose Alignment Network A vision based algorithm for active robot localization
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CEC NIPS 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR CVPR PAAP BMVC IJCAI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) WOWMOM MLSP ICRA ICASSP CASE IJCNN ACM Multimedia CIRA
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