Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks . In this paper we are motivated to propose resilient routing algorithms for wireless sensor networks . The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design . This means that speech can be produced in different types of environments where background noise can significantly degrade the perception of the synthetic message and consequently its intelligibility . The features discussed in this paper are being developed and assessed inside the EU funded SHARE project whose goal is to develop a multimodal communication system supporting rescue operations and disaster management . Deriving semantic 3D models of man-made environments hitherto has not reached the desired maturity which makes human interaction obsolete . Man-made environments play a central role in navigation , city planning , building management systems , disaster management or augmented reality . They are characterised by rich geometric and semantic structures . Disaster phenomenon disrupts human life which can be caused by nature or man-made incidence . Our objectives here are to : i ) visualize available data in resource allocations such as evacuation mapping , school allocation and management using data analytics , ii ) develop and design a mobile-based information system to support the disaster management process easier , and iii ) collect and analyze the demographic profile of the affected communities . We analyzed the existing process of data collection to design and develop a mobile-based system that employs Agile methods appropriate for system development . These approaches mainly suffer from low temporal resolution , incapability of sourcing broad range of required disaster data , accompanied with limited support for automated operations . Incorporating in situ sensing into the disaster management process can potentially address such challenges with providing wide-range of real-time data coupled with automated operations for data acquisition and management . In this research we study the process of integrating multi-sourced sensors for supporting natural disaster management , particularly an urban flood response . The framework allows the definition of : negotiation protocols , negotiation subjects composed of multiple negotiation issues , properties of negotiation issues , deal spaces , and utility functions of participant stakeholders . We show how this framework can be used for dynamic composition of workflows spanning multiple organizations in a disaster management information system . The framework allows the definition of : negotiation protocols , negotiation subjects composed of multiple negotiation issues , properties of negotiation issues , and utility functions of participant stakeholders . We show how this framework can be applied for service contracting in a disaster management information system . We provide initial experimental results concerning the effect of dynamic selection of negotiation protocol onto the quality of the negotiation outcome and the communication complexity of negotiation interactions . After that , various metadata parameters are investigated and complemented as the main part of SDI ’s technical development . Then , in the implementing part , requirements of Red Crescent Organization are assessed by gathering some analyzed layers and a spatial database of disaster management problem is created . Finally , as an example , functional outputs for managing and planning of rescue operations are presented completely . This speedup is a result of significant enhancements in the solution of the nonnegative least squares problem which regard ( a ) the development of analytical expressions for the evaluation of the gradient and objective functions and ( b ) a novel limited-memory quasi-Newton solution algorithm . Simulation results in the context of optimizing the performance of a disaster management problem using supervised learning verify the efficiency of the approach , achieving two orders of magnitude execution speedup and improved solution quality compared to state-of-the-art algorithms . Under such conditions , one of the issues is related to the efficient partner or team member selection as applicable in the case of collaborative networks . Although different partner selection mechanisms have been proposed in the literature of collaborative networks but considering the dynamic context of trust , these can not be applied directly in the disaster management situation . Trust and reputation have been identified as one of the important factors for the efficient disaster management in the related literature . In this paper , based on the probabilistic sensing model , we conduct comprehensive analytical and simulation studies on collaborative information coverage and object detection in wireless sensor networks . More specifically , we first define point information coverage and based on that we define p-coverage as a measure of the coverage performance for a randomly-deployed wireless sensor network . Then , we investigate the coverage and object detection performances of several simple decision fusion-based collaborative mechanisms and find that simple collaborations among active sensors indeed degrade the coverage performance due to the requirement of maintaining the target false detection probability . In this paper we propose a 3-phase algorithm based on Hamilton Circuit . Conducted simulation shows that the algorithm can improve coverage performance with less energy conservation . Experimental results verify the feasibility and validity of the algorithm proposed . Wireless communication is considered to be more challenging than the typical wired communication due to unpredictable channel conditions . In this paper , we target coverage area problem , where a group of sensors is selected from a set of sensors placed in a particular area to maximize the coverage provided to that area . The constraints to this optimization are the battery power of the sensor and number of sensors that are active at a given time . Wireless sensor networking is a rapidly merging research area . There are many areas where Wireless Sensor Network ( WSN ) applications are very important such as military , healthcare , agricultural and transportation . A wireless sensor network consists of low power devices which have limited computing resources . Thus , energy consumption is one of the most significant issues in wireless sensor network environment . For various sensor network applications , security features are also to be a rising big issue more and more . In addition , the simulations suggest that the concept of scenario-reduction may be useful to address computational tractability in ART and robust planning in general . CONCLUSIONS The simulations indicate that the adapted plans may improve target coverage and OAR protection at manageable adaptation and computational cost within the novel framework . In particular , adaptive strategies using Bayesian inference appear to perform best among all strategies . The MaxFoV problem . We consider the problem of target coverage in visual sensor networks with Pan-Tilt-Zoom ( PTZ ) cameras . The finely controllable movement in PTZ dimensions creates a large number of possible Field-of-View ( FoV ) settings , making it prohibitively expensive to consider them all in coverage algorithms . The activity scheduling of sensors to alternately wake up for sensing obligation such that the network lifetime can be efficiently prolonged is a very important issue in wireless sensor networks ( WSNs ) . Target area coverage is a new coverage problem in wireless sensor networks . The paper addresses the target area coverage problem and schedules sensors to alternatively wake up to collaboratively cover and sense the target area . As a device-free approach , radio tomographic imaging ( RTI ) is ideally suited for low-cost indoor localization in context-aware Internet-of-Things applications . However , the fundamental RTI algorithm relies on shadowing of the line of sight ( LOS ) links and therefore , conventional RTI implementations using 2.4 GHz sensing networks ( microRTI ) fail to accurately localize users in multipath-rich indoor environments . Visible Light Communication ( VLC- ) based localization techniques are very promising to fill this gap . In this paper , we propose Plugo , a novel VLC system with random multiple access towards low-cost indoor localization . Compared to conventional RF-based approaches that rely on dedicated wireless access points as location beacons , the proposed system has the potential to deliver better accuracies with reduced cost . Efforts to characterize the LC ’s function in humans using functional magnetic resonance imaging have been hampered by its small size and location near a large source of noise , the fourth ventricle . We tested whether the ability to characterize LC function is improved by employing neuromelanin-T1 weighted images ( nmT1 ) for LC localization and multi-echo functional magnetic resonance imaging ( ME-fMRI ) for estimating intrinsic functional connectivity ( iFC ) . Analyses indicated that , relative to a probabilistic atlas , utilizing nmT1 images to individually localize the LC increases the specificity of seed time series and clusters in the iFC maps . The implemented particle filter is a regularized and adaptive version . This filter handles thermals global localization and the underlying non-linear models , providing excellent results . The robust localization and tracking of faces in video streams is a fundamental concern for many subsequent multi-modal recognition approaches . Especially in meeting scenarios several independent processing queues often exist that use the position and gaze of faces , such as group action- and face recognizers . In this paper we investigate the time-frequency localization tradeoffs in practical implementation of filter bank multicarrier ( FBMC ) waveforms in short burst transmission . Frequency localization is considered as an important characteristic for future wireless communication systems , including 5 G , but it comes with the cost of not-so-good time localization . These goals are achieved through active ( and automatic ) reassignment of the available computational resources for on-line information processing . The methods of adaptive dependability enable – with no interruption of computational processes and while solving real-world problems – timely automatic detection and elimination of failures , faults of CMs and errors in the solution of specified problems through on-line localization of faulty modules and subsequent automatic reconfiguration of the system with the elimination of such modules from operation . Human eyebrows shape has good specificity and stability , hence precise localization outlines a series of threads for face expression analysis . This paper will introduce a simple method for eyebrow localization . The determined positions allow geometrical extraction of the upper face area , which will be further investigated for expression recognition . In this paper , we explicitly incorporate anchors ' misplacements into the distance measurement model and explore the pairwise sparse nature of the misplacements . We formulate the secure target localization problem as an ℓ1-regularized least squares ( LS ) problem , whose objective is to simultaneously locate the target as well as identify the compromised anchors . We establish the sparsity threshold which defines the upper bound for the number of identifiable malicious anchors , and propose a simple projected gradient search algorithm to solve this novel ℓ1-regularized LS problem in WSNs . The localization of active speakers with microphone arrays is an active research line with a considerable interest in many acoustic areas . Many algorithms for source localization are based on the computation of the Generalized Cross-Correlation function between microphone pairs employing phase transform weighting . As the complete breakdown of insulators would lead to power supply interruption and would cause a degraded power quality , the PD source localization process is much helpful to get rid of detrimental breakdown occurrence as the handling personnel can be warned about a probable breakdown source at a very premature state . Amongst many techniques , Time Delay of Arrival ( TDOA ) technique is a simple yet effective process to proceed for the PD source localization problem . Time delay estimation is an essential primary step to proceed for the localization process of a PD source using the TDOA technique . document : Quantized Densely Connected U - Nets for Efficient Landmark Localization In this paper , we propose quantized densely connected U - Nets for efficient visual landmark localization . The idea is that features of the same semantic meanings are globally reused across the stacked U - Nets . Locating visual landmarks , such as human body joints and facial key points , is an important yet challenging problem . The stacked U - Nets , e.g. hourglasses ( HGs ) , are widely used in landmark localization . Generally speaking , their success can be attributed to design patterns : 1 ) within each U - Net , connect the top - down and bottom - up feature blocks to encourage gradient flow ; and 2 ) stack multiple U - Nets in a cascade to refine prediction stage by stage . We further investigate an iterative refinement that may cut down half of the model size , by forwarding DU - Net twice using either detection or regression supervision . Different from previous efforts of quantizing only the model parameters , we are the first to quantize their inputs and gradients for better training efficiency on landmark localization tasks . By choosing appropriate quantization bit - widths for weights , inputs and gradients , quantized DU - Net achieves 75 % training memory saving with comparable performance . The best performing VRN is the one guided by detected landmarks ( VRN - Guided ) , however at the cost of higher computational complexity : VRN - Guided uses another stacked hourglass network for landmark localization . VRN - Multitask does not always perform particularly better than the plain VRN ( in fact on BU - 4DFE it performs worse ) , not justifying the increase of network complexity . [ reference ] shows the details of AP at different IoU thresholds . GHM - R loss slightly lowers the AP@IoU=0.5 but gains when the threshold is higher , which demonstrates our proposition that the so called easy examples in regression is important for accurate localization . subsubsection : Two - Stage Detector : In Section [ reference ] , we pursue a novel alternative direction based on coupling the recognition capacity of DCNNs and the fine - grained localization accuracy of fully connected CRFs and show that it is remarkably successful in addressing the localization challenge , producing accurate semantic segmentation results and recovering object boundaries at a level of detail that is well beyond the reach of existing methods . subsection : Fully - Connected Conditional Random Fields for Accurate Localization Traditionally , conditional random fields ( CRFs ) have been employed to smooth noisy segmentation maps rother2004grabcut , kohli2009robust . To examine if this is the case , we retrained our baseline model using binary cross - entropy on the same dilated ground truth we used for DeepFlux . Without context flux , performance drops to ( ) on SK - LARGE and to ( ) on SYM - PASCAL , validating the importance of our proposed representation for accurate localization . section : Conclusion This may cause the TLL network to output very close vertices , and high link scores between each candidate pairs , leading to confused detection results . Thus , the crowd occlusion severely increases the difficulty in pedestrian localization . In order to robustly localize each individual pedestrian in crowd scenes , locations of its surrounding objects need to be taken into account . [ ] exploit weakly annotated boxes via a segmentation infusion network to achieve considerable performance gains . However , occlusion still remains one of the most significant challenges in pedestrian detection , which increases the difficulty in pedestrian localization . Several methods use part - based model to describe the pedestrian in occlusion handling , which learn a series of part detectors and design some mechanisms to fuse the part detection results to localize partially occluded pedestrians . YOLO struggles to localize objects correctly . Localization errors account for more of YOLO ’s errors than all other sources combined . Fast R - CNN makes much fewer localization errors but far more background errors . 13.6 % of it The paper proposes a new integrated approach to object detection , recognition , and localization with a single ConvNet . We also introduce a novel method for localization and detection by accumulating predicted bounding boxes . We suggest that by combining many localization predictions , detection can be performed without training on background samples and that it is possible to avoid the time - consuming and complicated bootstrapping training passes . Approaches such as used region - level cues to infer the foreground segmentation mask and to discard the noisy visual information in the background . Chai et al . showed that jointly learning part localization and foreground segmentation together can be beneficial for fine - grained categorization . Similar to most previous part - based approaches , these efforts require the ground truth bounding box to initialize the segmentation seed . section : Introduction During the past few years , we have witnessed significant improvements in various face analysis tasks such as face detection and 2D facial landmark localization on static images . This is primarily attributed to the fact that the community has made a considerable effort to collect and annotate facial images captured under unconstrained conditions ( commonly referred to as “ in - the - wild ” ) and to the discriminative methodologies that can capitalise on the availability of such large amount of data . Following the standard PASCAL VOC protocol , Average Precision ( AP ) and the mean of AP ( mAP ) is the evaluation metric to test our model on the testing set . Correct Localization ( CorLoc ) is to test our model on the training set measuring the localization accuracy . All these two metrics are based on the PASCAL criterion ,
Wireless sensor networks play an important role in Internet of Things systems and services but are prone and vulnerable to poor communication channel quality and network attacks . In this paper we are motivated to propose resilient routing algorithms for wireless sensor networks . The main idea is to exploit the link reliability along with other traditional routing metrics for routing algorithm design .
This means that speech can be produced in different types of environments where background noise can significantly degrade the perception of the synthetic message and consequently its intelligibility . The features discussed in this paper are being developed and assessed inside the EU funded SHARE project whose goal is to develop a multimodal communication system supporting rescue operations and disaster management .
Deriving semantic 3D models of man-made environments hitherto has not reached the desired maturity which makes human interaction obsolete . Man-made environments play a central role in navigation , city planning , building management systems , disaster management or augmented reality . They are characterised by rich geometric and semantic structures .
Disaster phenomenon disrupts human life which can be caused by nature or man-made incidence . Our objectives here are to : i ) visualize available data in resource allocations such as evacuation mapping , school allocation and management using data analytics , ii ) develop and design a mobile-based information system to support the disaster management process easier , and iii ) collect and analyze the demographic profile of the affected communities . We analyzed the existing process of data collection to design and develop a mobile-based system that employs Agile methods appropriate for system development .
These approaches mainly suffer from low temporal resolution , incapability of sourcing broad range of required disaster data , accompanied with limited support for automated operations . Incorporating in situ sensing into the disaster management process can potentially address such challenges with providing wide-range of real-time data coupled with automated operations for data acquisition and management . In this research we study the process of integrating multi-sourced sensors for supporting natural disaster management , particularly an urban flood response .
The framework allows the definition of : negotiation protocols , negotiation subjects composed of multiple negotiation issues , properties of negotiation issues , deal spaces , and utility functions of participant stakeholders . We show how this framework can be used for dynamic composition of workflows spanning multiple organizations in a disaster management information system .
The framework allows the definition of : negotiation protocols , negotiation subjects composed of multiple negotiation issues , properties of negotiation issues , and utility functions of participant stakeholders . We show how this framework can be applied for service contracting in a disaster management information system . We provide initial experimental results concerning the effect of dynamic selection of negotiation protocol onto the quality of the negotiation outcome and the communication complexity of negotiation interactions .
After that , various metadata parameters are investigated and complemented as the main part of SDI ’s technical development . Then , in the implementing part , requirements of Red Crescent Organization are assessed by gathering some analyzed layers and a spatial database of disaster management problem is created . Finally , as an example , functional outputs for managing and planning of rescue operations are presented completely .
This speedup is a result of significant enhancements in the solution of the nonnegative least squares problem which regard ( a ) the development of analytical expressions for the evaluation of the gradient and objective functions and ( b ) a novel limited-memory quasi-Newton solution algorithm . Simulation results in the context of optimizing the performance of a disaster management problem using supervised learning verify the efficiency of the approach , achieving two orders of magnitude execution speedup and improved solution quality compared to state-of-the-art algorithms .
Under such conditions , one of the issues is related to the efficient partner or team member selection as applicable in the case of collaborative networks . Although different partner selection mechanisms have been proposed in the literature of collaborative networks but considering the dynamic context of trust , these can not be applied directly in the disaster management situation . Trust and reputation have been identified as one of the important factors for the efficient disaster management in the related literature .
In this paper , based on the probabilistic sensing model , we conduct comprehensive analytical and simulation studies on collaborative information coverage and object detection in wireless sensor networks . More specifically , we first define point information coverage and based on that we define p-coverage as a measure of the coverage performance for a randomly-deployed wireless sensor network . Then , we investigate the coverage and object detection performances of several simple decision fusion-based collaborative mechanisms and find that simple collaborations among active sensors indeed degrade the coverage performance due to the requirement of maintaining the target false detection probability .
In this paper we propose a 3-phase algorithm based on Hamilton Circuit . Conducted simulation shows that the algorithm can improve coverage performance with less energy conservation . Experimental results verify the feasibility and validity of the algorithm proposed .
Wireless communication is considered to be more challenging than the typical wired communication due to unpredictable channel conditions . In this paper , we target coverage area problem , where a group of sensors is selected from a set of sensors placed in a particular area to maximize the coverage provided to that area . The constraints to this optimization are the battery power of the sensor and number of sensors that are active at a given time .
Wireless sensor networking is a rapidly merging research area . There are many areas where Wireless Sensor Network ( WSN ) applications are very important such as military , healthcare , agricultural and transportation .
A wireless sensor network consists of low power devices which have limited computing resources . Thus , energy consumption is one of the most significant issues in wireless sensor network environment . For various sensor network applications , security features are also to be a rising big issue more and more .
In addition , the simulations suggest that the concept of scenario-reduction may be useful to address computational tractability in ART and robust planning in general . CONCLUSIONS The simulations indicate that the adapted plans may improve target coverage and OAR protection at manageable adaptation and computational cost within the novel framework . In particular , adaptive strategies using Bayesian inference appear to perform best among all strategies .
The MaxFoV problem . We consider the problem of target coverage in visual sensor networks with Pan-Tilt-Zoom ( PTZ ) cameras . The finely controllable movement in PTZ dimensions creates a large number of possible Field-of-View ( FoV ) settings , making it prohibitively expensive to consider them all in coverage algorithms .
The activity scheduling of sensors to alternately wake up for sensing obligation such that the network lifetime can be efficiently prolonged is a very important issue in wireless sensor networks ( WSNs ) . Target area coverage is a new coverage problem in wireless sensor networks . The paper addresses the target area coverage problem and schedules sensors to alternatively wake up to collaboratively cover and sense the target area .
As a device-free approach , radio tomographic imaging ( RTI ) is ideally suited for low-cost indoor localization in context-aware Internet-of-Things applications . However , the fundamental RTI algorithm relies on shadowing of the line of sight ( LOS ) links and therefore , conventional RTI implementations using 2.4 GHz sensing networks ( microRTI ) fail to accurately localize users in multipath-rich indoor environments .
Visible Light Communication ( VLC- ) based localization techniques are very promising to fill this gap . In this paper , we propose Plugo , a novel VLC system with random multiple access towards low-cost indoor localization . Compared to conventional RF-based approaches that rely on dedicated wireless access points as location beacons , the proposed system has the potential to deliver better accuracies with reduced cost .
Efforts to characterize the LC ’s function in humans using functional magnetic resonance imaging have been hampered by its small size and location near a large source of noise , the fourth ventricle . We tested whether the ability to characterize LC function is improved by employing neuromelanin-T1 weighted images ( nmT1 ) for LC localization and multi-echo functional magnetic resonance imaging ( ME-fMRI ) for estimating intrinsic functional connectivity ( iFC ) . Analyses indicated that , relative to a probabilistic atlas , utilizing nmT1 images to individually localize the LC increases the specificity of seed time series and clusters in the iFC maps .
The implemented particle filter is a regularized and adaptive version . This filter handles thermals global localization and the underlying non-linear models , providing excellent results .
The robust localization and tracking of faces in video streams is a fundamental concern for many subsequent multi-modal recognition approaches . Especially in meeting scenarios several independent processing queues often exist that use the position and gaze of faces , such as group action- and face recognizers .
In this paper we investigate the time-frequency localization tradeoffs in practical implementation of filter bank multicarrier ( FBMC ) waveforms in short burst transmission . Frequency localization is considered as an important characteristic for future wireless communication systems , including 5 G , but it comes with the cost of not-so-good time localization .
These goals are achieved through active ( and automatic ) reassignment of the available computational resources for on-line information processing . The methods of adaptive dependability enable – with no interruption of computational processes and while solving real-world problems – timely automatic detection and elimination of failures , faults of CMs and errors in the solution of specified problems through on-line localization of faulty modules and subsequent automatic reconfiguration of the system with the elimination of such modules from operation .
Human eyebrows shape has good specificity and stability , hence precise localization outlines a series of threads for face expression analysis . This paper will introduce a simple method for eyebrow localization . The determined positions allow geometrical extraction of the upper face area , which will be further investigated for expression recognition .
In this paper , we explicitly incorporate anchors ' misplacements into the distance measurement model and explore the pairwise sparse nature of the misplacements . We formulate the secure target localization problem as an ℓ1-regularized least squares ( LS ) problem , whose objective is to simultaneously locate the target as well as identify the compromised anchors . We establish the sparsity threshold which defines the upper bound for the number of identifiable malicious anchors , and propose a simple projected gradient search algorithm to solve this novel ℓ1-regularized LS problem in WSNs .
The localization of active speakers with microphone arrays is an active research line with a considerable interest in many acoustic areas . Many algorithms for source localization are based on the computation of the Generalized Cross-Correlation function between microphone pairs employing phase transform weighting .
As the complete breakdown of insulators would lead to power supply interruption and would cause a degraded power quality , the PD source localization process is much helpful to get rid of detrimental breakdown occurrence as the handling personnel can be warned about a probable breakdown source at a very premature state . Amongst many techniques , Time Delay of Arrival ( TDOA ) technique is a simple yet effective process to proceed for the PD source localization problem . Time delay estimation is an essential primary step to proceed for the localization process of a PD source using the TDOA technique .
document : Quantized Densely Connected U - Nets for Efficient Landmark Localization In this paper , we propose quantized densely connected U - Nets for efficient visual landmark localization . The idea is that features of the same semantic meanings are globally reused across the stacked U - Nets .
Locating visual landmarks , such as human body joints and facial key points , is an important yet challenging problem . The stacked U - Nets , e.g. hourglasses ( HGs ) , are widely used in landmark localization . Generally speaking , their success can be attributed to design patterns : 1 ) within each U - Net , connect the top - down and bottom - up feature blocks to encourage gradient flow ; and 2 ) stack multiple U - Nets in a cascade to refine prediction stage by stage .
We further investigate an iterative refinement that may cut down half of the model size , by forwarding DU - Net twice using either detection or regression supervision . Different from previous efforts of quantizing only the model parameters , we are the first to quantize their inputs and gradients for better training efficiency on landmark localization tasks . By choosing appropriate quantization bit - widths for weights , inputs and gradients , quantized DU - Net achieves 75 % training memory saving with comparable performance .
The best performing VRN is the one guided by detected landmarks ( VRN - Guided ) , however at the cost of higher computational complexity : VRN - Guided uses another stacked hourglass network for landmark localization . VRN - Multitask does not always perform particularly better than the plain VRN ( in fact on BU - 4DFE it performs worse ) , not justifying the increase of network complexity .
[ reference ] shows the details of AP at different IoU thresholds . GHM - R loss slightly lowers the AP@IoU=0.5 but gains when the threshold is higher , which demonstrates our proposition that the so called easy examples in regression is important for accurate localization . subsubsection : Two - Stage Detector :
In Section [ reference ] , we pursue a novel alternative direction based on coupling the recognition capacity of DCNNs and the fine - grained localization accuracy of fully connected CRFs and show that it is remarkably successful in addressing the localization challenge , producing accurate semantic segmentation results and recovering object boundaries at a level of detail that is well beyond the reach of existing methods . subsection : Fully - Connected Conditional Random Fields for Accurate Localization Traditionally , conditional random fields ( CRFs ) have been employed to smooth noisy segmentation maps rother2004grabcut , kohli2009robust .
To examine if this is the case , we retrained our baseline model using binary cross - entropy on the same dilated ground truth we used for DeepFlux . Without context flux , performance drops to ( ) on SK - LARGE and to ( ) on SYM - PASCAL , validating the importance of our proposed representation for accurate localization . section : Conclusion
This may cause the TLL network to output very close vertices , and high link scores between each candidate pairs , leading to confused detection results . Thus , the crowd occlusion severely increases the difficulty in pedestrian localization . In order to robustly localize each individual pedestrian in crowd scenes , locations of its surrounding objects need to be taken into account .
[ ] exploit weakly annotated boxes via a segmentation infusion network to achieve considerable performance gains . However , occlusion still remains one of the most significant challenges in pedestrian detection , which increases the difficulty in pedestrian localization . Several methods use part - based model to describe the pedestrian in occlusion handling , which learn a series of part detectors and design some mechanisms to fuse the part detection results to localize partially occluded pedestrians .
YOLO struggles to localize objects correctly . Localization errors account for more of YOLO ’s errors than all other sources combined . Fast R - CNN makes much fewer localization errors but far more background errors . 13.6 % of it
The paper proposes a new integrated approach to object detection , recognition , and localization with a single ConvNet . We also introduce a novel method for localization and detection by accumulating predicted bounding boxes . We suggest that by combining many localization predictions , detection can be performed without training on background samples and that it is possible to avoid the time - consuming and complicated bootstrapping training passes .
Approaches such as used region - level cues to infer the foreground segmentation mask and to discard the noisy visual information in the background . Chai et al . showed that jointly learning part localization and foreground segmentation together can be beneficial for fine - grained categorization . Similar to most previous part - based approaches , these efforts require the ground truth bounding box to initialize the segmentation seed .
section : Introduction During the past few years , we have witnessed significant improvements in various face analysis tasks such as face detection and 2D facial landmark localization on static images . This is primarily attributed to the fact that the community has made a considerable effort to collect and annotate facial images captured under unconstrained conditions ( commonly referred to as “ in - the - wild ” ) and to the discriminative methodologies that can capitalise on the availability of such large amount of data .
Following the standard PASCAL VOC protocol , Average Precision ( AP ) and the mean of AP ( mAP ) is the evaluation metric to test our model on the testing set . Correct Localization ( CorLoc ) is to test our model on the training set measuring the localization accuracy . All these two metrics are based on the PASCAL criterion ,
Resilient Routing Mechanism for Wireless Sensor Networks With Deep Learning Link Reliability Prediction Speech synthesis enhancement in noisy environments A future for learning semantic models of man-made environments Disaster Response Visualization Analysis Employing Data Analytics in Resource Allocation Towards integrating multi-sourced sensors for natural disaster management - Case study in flood management Multi-Agent Systems, Ontologies and Negotiation for Dynamic Service Composition in Multi-Organizational Environmental Management Dynamic Selection of Negotiation Protocol in Multi-agent Systems for Disaster Management Coordinating Disaster Management Procedures Using Spatial Search engine and Metadata in SDI Framework FAST NON-NEGATIVE LEAST-SQUARES LEARNING IN THE RANDOM NEURAL NETWORK A reputation management system for efficient selection of disaster management team Analytical Study of Collaborative Information Coverage for Object Detection in Sensor Networks Hamilton Circuit-Based Algorithm for Improving Coverage of Hybrid Sensor Network Online optimization of wireless sensors selection over an unknown stochastic environment Location aware queries for sensor network Security Architecture for IEEE 802.15.4-based Wireless Sensor Network On Adaptation Cost and Tractability in Robust Adaptive Radiation Therapy Optimization. Coverage in visual sensor networks with Pan-Tilt-Zoom cameras: The MaxFoV problem Preserving Target Area Coverage in Wireless Sensor Networks by Using Computational Geometry mmRTI: Radio tomographic imaging using highly-directional millimeter-wave devices for accurate and robust indoor localization Plugo: A Scalable Visible Light Communication System Towards Low-Cost Indoor Localization Estimates of locus coeruleus function with functional magnetic resonance imaging are influenced by localization approaches and the use of multi-echo data Thermal localization Face tracking in meeting room scenarios using omnidirectional views Optimized burst truncation in fast-convolution filter bank based waveform generation Адаптивная гарантоспособность информационных систем управления Eyebrows localization for expression analysis A novel ℓ1-regularized LS formulation for target localization and malicious anchor identification A Bayesian inference model for speech localization (L). Mutual Information Based Time Delay Estimation of Ultra High Frequency Signals Radiated from Partial Discharge Sources Quantized Densely Connected U-Nets for Efficient Landmark Localization Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Gradient Harmonized Single-stage Detector Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs DeepFlux for Skeletons in the Wild Small-scale Pedestrian Detection Based on Somatic Topology Localization and Temporal Feature Aggregation Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd You Only Look Once: Unified, Real-Time Object Detection OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks Part-Based R-CNNs for Fine-Grained Category Detection 3D Face Morphable Models "In-the-Wild" PCL: Proposal Cluster Learning for Weakly Supervised Object Detection
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INTERSPEECH ICPR Software Agents, Agent Systems and Their Applications ICCCI (2) SECON MSN SIU INFOCOM WCNC PIMRC IROS ICPR (4) SPAWC ICCP MILCOM ECCV 2017 IEEE International Conference on Computer Vision (ICCV) AAAI ICLR 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ArXiv ECCV 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ICLR ECCV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IEEE Transactions on Pattern Analysis and Machine Intelligence
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