It is challenging due to the varied visual appearance from illumination , view angle , background , and possible occlusions , leading to the difficulties when measuring the relevance , i.e. , similarities , between probe and gallery images . Existing methods mainly focus on pairwise distance metric learning for person reidentification . In practice , pairwise image matching may limit the data for comparison ( just the probe and one gallery subject ) and yet lead to suboptimal results . Person reidentification ( re-id ) , an emerging problem in visual surveillance , deals with maintaining the identities of individuals while they traverse various locations surveilled by a camera network . Motivated by real-world scenarios , we propose a method that seeks to simultaneously identify who among a group of individuals viewed in one view are present/absent in the other . The question of when person detections in different views or at different time instants can be linked to the same individual is of fundamental importance to the video analysis in large-scale network of cameras . This is the person reidentification problem . The paper focuses on algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait . We address the person reidentification problem by efficient data representation method . Based on the Relaxed Nonnegative matrix factorization ( rNMF ) which has no sign constraints on the data matrix and the basis matrix , we consider two regularizations to improve the Relaxed NMF , which are the local manifold assumption and a rank constraint . In video-surveillance , person re- identication is the task of recognising whether an individual has already been observed over a network of cameras . Typically , this is achieved by exploiting the clothing appearance , as classical biometric traits like the face are impractical in real-world video surveil- lance scenarios . Clothing appearance is represented by means of low-level local and/or global features of the image , usually extracted according to some part- based body model to treat dierent body parts ( e.g. torso and legs ) independently . This paper provides a comprehensive review of current approaches to build appearance descriptors for person re-identication . The most relevant techniques are described in detail , and categorised according to the body models and features used . This paper proposes a novel image representation which can properly handle both background and illumination variations . It is therefore adapted to the person/face reidentification tasks , avoiding the use of any additional pre-processing steps such as foreground-background separation or face and body part segmentation . This novel representation relies on the combination of Biologically Inspired Features ( BIF ) and covariance descriptors used to compute the similarity of the BIF features at neighboring scales . In the field of video surveillance , person re-ID ( reidentification ) is an assignment of identifying an individual caught by various cameras in the system at various place and time . With the developing security dangers , this undertaking has an incredible significance in observation out in the open spots like air terminal , railroad station , shopping buildings , and so forth . However , the softmax layer can not be extended to unknown classes because of its closed nature , so the classification model is just regarded as the feature extractor . To overcome the problem mentioned above and make the person re-identification process end-to-end , this paper cast the person re-identification into a regression process and calculates the probability that persons in the images belong to the same identity . First , this paper proposes a deep regression model , named deep regression neural network integrating adaptive multi-attribute fusion method ( DRNN-AMAF ) , which can make the person re-identification as regression analysis . Furthermore , relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations . We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions . These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes . In this research study , we have proposed an appearance-based classifier to classify human corresponding to their attire and clothing . Personality re-identification proposes to address the issue of keeping track of persons . Our proposed methodology presents the advance work on both CUHK03 and CUHK01 dataset , and bypass over-fitting problem by artificially enlarging the dataset using label-preserving transformations . Despite this , re-identifying groups of people compound the common single person re-identification problems by introducing changes in the relative position of persons within the group and severe self-occlusions . In this paper , we propose a solution for group re-identification that grounds on transferring knowledge from single person reidentification to group re-identification by exploiting sparse dictionary learning . First , a dictionary of sparse atoms is learned using patches extracted from single person images . document : Pose - driven Deep Convolutional Model for Person Re - identification Feature extraction and matching are two crucial components in person Re - Identification ( ReID ) . The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images . document : A Discriminatively Learned CNN Embedding for Person Re - identification In this paper , we revisit two popular convolutional neural networks ( CNN ) in person re - identification ( re - ID ) , i.e. , verification and identification models . The two models have their respective advantages and limitations due to different loss functions . document : Unlabeled Samples Generated by GAN Improve the Person Re - identification Baseline in vitro The main contribution of this paper is a simple semi - supervised pipeline that only uses the original training set without collecting extra data . For feature representation , several effective approaches have been proposed , for example , the ensemble of local features ( ELF ) , SDALF , kBiCov , fisher vectors ( LDFV ) , salience match , and mid - level filter . These hand - crafted or learning based descriptors have made impressive improvements over robust feature representation , and advanced the person re - identification research . However , how to design or learn a robust feature for the person re - identification challenge still remains an open problem . Another aspect of person re - identification is how to learn a robust distance or similarity function to deal with the complex matching problem . Li at al . proposed the learning of Locally - Adaptive Decision Functions ( LADF ) for person verification , which can be viewed as a joint model of a distance metric and a locally adapted thresholding rule . Prosser et al . formulated the person re - identification problem as a ranking problem , and applied the RankSVM to learn a subspace . In , local experts were considered to learn a common feature space for person re - identification across views . subsection : Experiments on VIPeR VIPeR is a challenging person re - identification database that has been widely used for benchmark evaluation . It contains 632 pairs of person images , captured by a pair of cameras in an outdoor environment . subsubsection : Comparison of Features Next , we compared the proposed LOMO feature with other three available person re - identification features . The first feature is called Ensemble of Local Features ( ELF ) , proposed in , and later modified by . Recently , person re-identification technique has been successfully applied to many fields , such as suspect tracking and lost human location . As video always contains more valuable information , more and more researchers focus on video based person re-identification , especially in image-to-video person re-identification ( IVPR ) . However , most of existing IVPR models are under the supervised framework . In the proposed framework , the feature representation and the similarity metric learning can be learned and optimized simultaneously . We evaluated the proposed framework on three public person re-id data sets , and the experimental results showed that the proposed approach is effective for the image-to-video person re-id . In fact , image-to-video person re-id has important significance in lost-human location , criminal-tracking , and pedestrian video retrieval . In image-to-video person re-id task , the key challenge of this issue is how to build an accurate connection between appearance image features and spatio-temporal video features due to the huge cross-media gap in different modalities . Although existing image-to-video person re-id models have achieved good effectiveness , there is still a large distance away from practical application . Besides , our network can alleviate the inherent cross-media gap by a novel media-pulling constraint term . The extensive experiments are conducted on three large scale datasets ( Market1501 , Mars and CUHK03 ) and two small datasets ( PRID-2011 , iLIDS-VID ) , and the results show our CBAN approach can solve the image-to-video person re-id problem effectively with a body-part attention mechanism . Extensive experiments conducted on public datasets demonstrate that our proposed model achieves the state-of-the-art with higher efficiency . Moreover , the proposed MKTB and GRB are plug-and-play modules and the experiments on other tasks , like video understanding and video-based person reidentification , also display their good performance in efficiency and capability of generalization . Because of its distinction and reliability , the person-salient region has been applied to pedestrian re-identification ( re-id ) across disjoint camera views . Despite the great progress achieved , a few works have studied the more challenging image-to-video person re-id problem , in which the gallery consists of videos and the same pedestrian appears in a continuous video sequence . The intrinsic high redundancy of such video sequences makes it more difficult to obtain high performance for pedestrian re-id . In recent years , person re-identification community obtained large size of annotated datasets and deep learning architecture based approaches have obtained significant improvement in the accuracy over the years as compared to hand-crafted approaches . In this survey paper , we have classified deep learning based approaches into two categories , i.e. , image-based and video-based person re-identification . We have also presented the currently ongoing under developing works , issues and future directions for person re-identification . Abstract . Recently , image-to-video person reidentification ( IVPR ) has attracted enormous research interest , and various models are proposed . IVPR is often applied to urgent situations , such as suspect tracking and lost-human locating . Existing IVPR models are under supervised frameworks , which require a large number of labeled image-to-video pairs . This severely limits their real-time efficiency in urgent situations , because annotation is much more time-consuming . To solve the urgent image-to-video person reidentification ( UIVPR ) problem , we propose a cross-media transfer cycle generative adversarial networks ( CTC-GAN ) network . Our model aims to alleviate the “ media-gap ” between image-to-video pairs without newly labeled pairs . With back propagation , temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated . With additional classification and integrated triplet losses , our model can learn expressive and discriminative image and video features for image-to-video re-identification . Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin . assumption , we provide an error bound that sheds light upon how could we improve VPRe-id . Our work also presents a promising way to bridge the gap between video and image based person re-identification . Comprehensive experimental evaluations demonstrate that the proposed solution achieves state-of-the-art performances on multiple widely used datasets ( iLIDS-VID , PRID 2011 , and MARS ) . Xiao propose the domain guided dropout to learn features over multiple datasets simultaneously with identity classification loss . Zheng learn the IDE feature for the video - based person re - identification . Xiao and Zheng learn the IDE feature to jointly solve the pedestrian detection and person ReID tasks . In addition , the proposed objective function can be solved by taking advantage of simple matrix multiplications and hence can avoid computationally complex optimization schemes . The proposed algorithm termed largest center-specific margin metric learning is shown to be computationally efficient and can be applied to large-scale person re-identification . Extensive experiments carried out on two challenging large-scale databases ( CUHK03 and Market1501 ) demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches . Many real-world applications , such as city scale traffic monitoring and control , requires large scale re-identification . However , previous ReID methods often failed to address two limitations in existing ReID benchmarks , i.e. , low spatiotem-poral coverage and sample imbalance . In recent years , a variety of proposed methods based on deep convolutional neural networks ( CNNs ) have improved the state of the art for large-scale person re-identification ( ReID ) . While a large number of optimizations and network improvements have been proposed , there has been relatively little evaluation of the influence of training data and baseline network architecture . Group re-identification ( G-ReID ) is a less-studied task . Its challenges include not only appearance changes of individuals which have been well-investigated in general person re-identification ( ReID ) , but also group layout changes and group membership changes which are newly introduced by G-ReID . The key task of G-ReID is to learn representations robust to these changes . Group re-identification ( G-ReID ) is an important yet less-studied task . Its challenges not only lie in appearance changes of individuals which have been well-investigated in general person re-identification ( ReID ) , but also derive from group layout and membership changes . So the key task of G-ReID is to learn representations robust to such changes .
It is challenging due to the varied visual appearance from illumination , view angle , background , and possible occlusions , leading to the difficulties when measuring the relevance , i.e. , similarities , between probe and gallery images . Existing methods mainly focus on pairwise distance metric learning for person reidentification . In practice , pairwise image matching may limit the data for comparison ( just the probe and one gallery subject ) and yet lead to suboptimal results .
Person reidentification ( re-id ) , an emerging problem in visual surveillance , deals with maintaining the identities of individuals while they traverse various locations surveilled by a camera network . Motivated by real-world scenarios , we propose a method that seeks to simultaneously identify who among a group of individuals viewed in one view are present/absent in the other .
The question of when person detections in different views or at different time instants can be linked to the same individual is of fundamental importance to the video analysis in large-scale network of cameras . This is the person reidentification problem . The paper focuses on algorithms that use the overall appearance of an individual as opposed to passive biometrics such as face and gait .
We address the person reidentification problem by efficient data representation method . Based on the Relaxed Nonnegative matrix factorization ( rNMF ) which has no sign constraints on the data matrix and the basis matrix , we consider two regularizations to improve the Relaxed NMF , which are the local manifold assumption and a rank constraint .
In video-surveillance , person re- identication is the task of recognising whether an individual has already been observed over a network of cameras . Typically , this is achieved by exploiting the clothing appearance , as classical biometric traits like the face are impractical in real-world video surveil- lance scenarios . Clothing appearance is represented by means of low-level local and/or global features of the image , usually extracted according to some part- based body model to treat dierent body parts ( e.g. torso and legs ) independently . This paper provides a comprehensive review of current approaches to build appearance descriptors for person re-identication . The most relevant techniques are described in detail , and categorised according to the body models and features used .
This paper proposes a novel image representation which can properly handle both background and illumination variations . It is therefore adapted to the person/face reidentification tasks , avoiding the use of any additional pre-processing steps such as foreground-background separation or face and body part segmentation . This novel representation relies on the combination of Biologically Inspired Features ( BIF ) and covariance descriptors used to compute the similarity of the BIF features at neighboring scales .
In the field of video surveillance , person re-ID ( reidentification ) is an assignment of identifying an individual caught by various cameras in the system at various place and time . With the developing security dangers , this undertaking has an incredible significance in observation out in the open spots like air terminal , railroad station , shopping buildings , and so forth .
However , the softmax layer can not be extended to unknown classes because of its closed nature , so the classification model is just regarded as the feature extractor . To overcome the problem mentioned above and make the person re-identification process end-to-end , this paper cast the person re-identification into a regression process and calculates the probability that persons in the images belong to the same identity . First , this paper proposes a deep regression model , named deep regression neural network integrating adaptive multi-attribute fusion method ( DRNN-AMAF ) , which can make the person re-identification as regression analysis .
Furthermore , relying on a small training-dataset would easily lead to poor performance due to the large intraclass and interclass variations . We annotated large scale people images collected from different person reidentification benchmarks covering a large attribute sample and reflecting the challenges of uncontrolled acquisition conditions . These annotations were gathered into an appearance semantic attribute dataset that contains 3590 images annotated with 14 attributes .
In this research study , we have proposed an appearance-based classifier to classify human corresponding to their attire and clothing . Personality re-identification proposes to address the issue of keeping track of persons . Our proposed methodology presents the advance work on both CUHK03 and CUHK01 dataset , and bypass over-fitting problem by artificially enlarging the dataset using label-preserving transformations .
Despite this , re-identifying groups of people compound the common single person re-identification problems by introducing changes in the relative position of persons within the group and severe self-occlusions . In this paper , we propose a solution for group re-identification that grounds on transferring knowledge from single person reidentification to group re-identification by exploiting sparse dictionary learning . First , a dictionary of sparse atoms is learned using patches extracted from single person images .
document : Pose - driven Deep Convolutional Model for Person Re - identification Feature extraction and matching are two crucial components in person Re - Identification ( ReID ) . The large pose deformations and the complex view variations exhibited by the captured person images significantly increase the difficulty of learning and matching of the features from person images .
document : A Discriminatively Learned CNN Embedding for Person Re - identification In this paper , we revisit two popular convolutional neural networks ( CNN ) in person re - identification ( re - ID ) , i.e. , verification and identification models . The two models have their respective advantages and limitations due to different loss functions .
document : Unlabeled Samples Generated by GAN Improve the Person Re - identification Baseline in vitro The main contribution of this paper is a simple semi - supervised pipeline that only uses the original training set without collecting extra data .
For feature representation , several effective approaches have been proposed , for example , the ensemble of local features ( ELF ) , SDALF , kBiCov , fisher vectors ( LDFV ) , salience match , and mid - level filter . These hand - crafted or learning based descriptors have made impressive improvements over robust feature representation , and advanced the person re - identification research . However , how to design or learn a robust feature for the person re - identification challenge still remains an open problem . Another aspect of person re - identification is how to learn a robust distance or similarity function to deal with the complex matching problem .
Li at al . proposed the learning of Locally - Adaptive Decision Functions ( LADF ) for person verification , which can be viewed as a joint model of a distance metric and a locally adapted thresholding rule . Prosser et al . formulated the person re - identification problem as a ranking problem , and applied the RankSVM to learn a subspace . In , local experts were considered to learn a common feature space for person re - identification across views .
subsection : Experiments on VIPeR VIPeR is a challenging person re - identification database that has been widely used for benchmark evaluation . It contains 632 pairs of person images , captured by a pair of cameras in an outdoor environment .
subsubsection : Comparison of Features Next , we compared the proposed LOMO feature with other three available person re - identification features . The first feature is called Ensemble of Local Features ( ELF ) , proposed in , and later modified by .
Recently , person re-identification technique has been successfully applied to many fields , such as suspect tracking and lost human location . As video always contains more valuable information , more and more researchers focus on video based person re-identification , especially in image-to-video person re-identification ( IVPR ) . However , most of existing IVPR models are under the supervised framework .
In the proposed framework , the feature representation and the similarity metric learning can be learned and optimized simultaneously . We evaluated the proposed framework on three public person re-id data sets , and the experimental results showed that the proposed approach is effective for the image-to-video person re-id .
In fact , image-to-video person re-id has important significance in lost-human location , criminal-tracking , and pedestrian video retrieval . In image-to-video person re-id task , the key challenge of this issue is how to build an accurate connection between appearance image features and spatio-temporal video features due to the huge cross-media gap in different modalities . Although existing image-to-video person re-id models have achieved good effectiveness , there is still a large distance away from practical application .
Besides , our network can alleviate the inherent cross-media gap by a novel media-pulling constraint term . The extensive experiments are conducted on three large scale datasets ( Market1501 , Mars and CUHK03 ) and two small datasets ( PRID-2011 , iLIDS-VID ) , and the results show our CBAN approach can solve the image-to-video person re-id problem effectively with a body-part attention mechanism .
Extensive experiments conducted on public datasets demonstrate that our proposed model achieves the state-of-the-art with higher efficiency . Moreover , the proposed MKTB and GRB are plug-and-play modules and the experiments on other tasks , like video understanding and video-based person reidentification , also display their good performance in efficiency and capability of generalization .
Because of its distinction and reliability , the person-salient region has been applied to pedestrian re-identification ( re-id ) across disjoint camera views . Despite the great progress achieved , a few works have studied the more challenging image-to-video person re-id problem , in which the gallery consists of videos and the same pedestrian appears in a continuous video sequence . The intrinsic high redundancy of such video sequences makes it more difficult to obtain high performance for pedestrian re-id .
In recent years , person re-identification community obtained large size of annotated datasets and deep learning architecture based approaches have obtained significant improvement in the accuracy over the years as compared to hand-crafted approaches . In this survey paper , we have classified deep learning based approaches into two categories , i.e. , image-based and video-based person re-identification . We have also presented the currently ongoing under developing works , issues and future directions for person re-identification .
Abstract . Recently , image-to-video person reidentification ( IVPR ) has attracted enormous research interest , and various models are proposed . IVPR is often applied to urgent situations , such as suspect tracking and lost-human locating . Existing IVPR models are under supervised frameworks , which require a large number of labeled image-to-video pairs . This severely limits their real-time efficiency in urgent situations , because annotation is much more time-consuming . To solve the urgent image-to-video person reidentification ( UIVPR ) problem , we propose a cross-media transfer cycle generative adversarial networks ( CTC-GAN ) network . Our model aims to alleviate the “ media-gap ” between image-to-video pairs without newly labeled pairs .
With back propagation , temporal knowledge can be transferred to enhance the image features and the information asymmetry problem can be alleviated . With additional classification and integrated triplet losses , our model can learn expressive and discriminative image and video features for image-to-video re-identification . Extensive experiments demonstrate the effectiveness of our method and the overall results on two widely used datasets surpass the state-of-the-art methods by a large margin .
assumption , we provide an error bound that sheds light upon how could we improve VPRe-id . Our work also presents a promising way to bridge the gap between video and image based person re-identification . Comprehensive experimental evaluations demonstrate that the proposed solution achieves state-of-the-art performances on multiple widely used datasets ( iLIDS-VID , PRID 2011 , and MARS ) .
Xiao propose the domain guided dropout to learn features over multiple datasets simultaneously with identity classification loss . Zheng learn the IDE feature for the video - based person re - identification . Xiao and Zheng learn the IDE feature to jointly solve the pedestrian detection and person ReID tasks .
In addition , the proposed objective function can be solved by taking advantage of simple matrix multiplications and hence can avoid computationally complex optimization schemes . The proposed algorithm termed largest center-specific margin metric learning is shown to be computationally efficient and can be applied to large-scale person re-identification . Extensive experiments carried out on two challenging large-scale databases ( CUHK03 and Market1501 ) demonstrate that the proposed algorithm performs favorably against the state-of-the-art approaches .
Many real-world applications , such as city scale traffic monitoring and control , requires large scale re-identification . However , previous ReID methods often failed to address two limitations in existing ReID benchmarks , i.e. , low spatiotem-poral coverage and sample imbalance .
In recent years , a variety of proposed methods based on deep convolutional neural networks ( CNNs ) have improved the state of the art for large-scale person re-identification ( ReID ) . While a large number of optimizations and network improvements have been proposed , there has been relatively little evaluation of the influence of training data and baseline network architecture .
Group re-identification ( G-ReID ) is a less-studied task . Its challenges include not only appearance changes of individuals which have been well-investigated in general person re-identification ( ReID ) , but also group layout changes and group membership changes which are newly introduced by G-ReID . The key task of G-ReID is to learn representations robust to these changes .
Group re-identification ( G-ReID ) is an important yet less-studied task . Its challenges not only lie in appearance changes of individuals which have been well-investigated in general person re-identification ( ReID ) , but also derive from group layout and membership changes . So the key task of G-ReID is to learn representations robust to such changes .
Beyond Pairwise Matching: Person Reidentification via High-Order Relevance Learning PRISM: Person Reidentification via Structured Matching Appearance-based person reidentification in camera networks: problem overview and current approaches Person Re-identification Based on Relaxed Nonnegative Matrix Factorization with Regularizations Appearance Descriptors for Person Re-identification: a Comprehensive Review BiCov: a novel image representation for person re-identification and face verification A Survey on Recent Approaches in Person Re-ID Deep Regression Neural Network for End-to-End Person Re-Identification Semantic attributes for people’s appearance description: an appearance modality for video surveillance applications Cross Input Neighbourhood Difference for Re-identification of Occupational Humans Group Re-identification via Unsupervised Transfer of Sparse Features Encoding Pose-Driven Deep Convolutional Model for Person Re-identification A Discriminatively Learned CNN Embedding for Person Reidentification Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in Vitro Person re-identification by Local Maximal Occurrence representation and metric learning Unsupervised domain adaption for image-to-video person re-identification Image-to-Video Person Re-Identification With Temporally Memorized Similarity Learning Cross-Media Body-Part Attention Network for Image-to-Video Person Re-Identification High Performance Gesture Recognition via Effective and Efficient Temporal Modeling Salient Region-Based Least-Squares Log-Density Gradient Clustering for Image-To-Video Person Re-Identification A Study on Deep Convolutional Neural Network Based Approaches for Person Re-identification Urgent image-to-video person reidentification by cross-media transfer cycle generative adversarial networks Temporal Knowledge Propagation for Image-to-Video Person Re-Identification Ordered or Orderless: A Revisit for Video based Person Re-Identification Learning Deep Context-Aware Features over Body and Latent Parts for Person Re-identification Modeling Unknown Class Centers for Metric Learning on Person Re-Identification Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification Group Re-Identification via Transferred Representation and Adaptive Fusion Group Re-identification via Transferred Single and Couple Representation Learning
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2018 2014 2011 2014 2013 2012 2017 2019 2017 2019 2017 2017 2018 2017 2015 2020 2018 2019 2019 2018 2017 2019 2019 2019 2017 2018 2019 2018 2019 2019
ICPR BMVC 2017 IEEE International Conference on Computer Vision (ICCV) ACM Trans. Multim. Comput. Commun. Appl. 2017 IEEE International Conference on Computer Vision (ICCV) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IJCAI PReMI 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) CRV BigMM
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