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20
B.: Local fisher discriminant analysis for pedestrian re-identification
- In: CVPR
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Salient Color Names for Person Re-identification
"... Abstract. Color naming, which relates colors with color names, can help people with a semantic analysis of images in many computer vision applications. In this paper, we propose a novel salient color names based color descriptor (SCNCD) to describe colors. SCNCD utilizes salient color names to guara ..."
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Cited by 13 (3 self)
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Abstract. Color naming, which relates colors with color names, can help people with a semantic analysis of images in many computer vision applications. In this paper, we propose a novel salient color names based color descriptor (SCNCD) to describe colors. SCNCD utilizes salient color names to guarantee that a higher probability will be assigned to the color name which is nearer to the color. Based on SCNCD, color distributions over color names in different color spaces are then ob-tained and fused to generate a feature representation. Moreover, the effect of background information is employed and analyzed for person re-identification. With a simple metric learning method, the proposed approach outperforms the state-of-the-art performance (without user’s feedback optimization) on two challenging datasets (VIPeR and PRID 450S). More importantly, the proposed feature can be obtained very fast if we compute SCNCD of each color in advance.
Viewpoint invariant human re-identification in camera networks using pose priors and subject-discriminative features
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2013
"... Human re-identification across cameras with non-overlapping fields of view is one of the most important and difficult problems in video surveillance and analysis. However, current algorithms are likely to fail in real-world scenarios for several reasons. For example, surveillance cameras are typical ..."
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Cited by 6 (4 self)
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Human re-identification across cameras with non-overlapping fields of view is one of the most important and difficult problems in video surveillance and analysis. However, current algorithms are likely to fail in real-world scenarios for several reasons. For example, surveillance cameras are typically mounted high above the ground plane, causing serious perspective changes. Also, most algorithms approach matching across images using the same descriptors, regardless of camera viewpoint or human pose. Here, we introduce a re-identification algorithm that addresses both problems. We build a model for human appearance as a function of pose, using training data gathered from a calibrated camera. We then apply this “pose prior” in online re-identification to make matching and identification more robust to viewpoint. We further integrate person-specific features learned over the course of tracking to improve the algorithm’s performance. We evaluate the performance of the proposed algorithm and compare it to several state-of-the-art algorithms, demonstrating superior performance on standard benchmarking datasets as well as a challenging new airport surveillance scenario.
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
"... Person re-identification is an important technique to-wards automatic search of a person’s presence in a surveil-lance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be ro-bust to illum ..."
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Cited by 3 (1 self)
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Person re-identification is an important technique to-wards automatic search of a person’s presence in a surveil-lance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be ro-bust to illumination and viewpoint changes, and a discrim-inant metric should be learned to match various person im-ages. In this paper, we propose an effective feature repre-sentation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO fea-ture analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable represen-tation against viewpoint changes. Besides, to handle illu-mination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimension-al subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 iden-tification rates by 2.2%, 4.88%, 28.91%, and 31.55 % on the four databases, respectively. 1.
Re-Identification in the Function Space of Feature Warps
, 2014
"... Person re-identification in a non-overlapping multicamera scenario is an open challenge in computer vision because of the large changes in appearances caused by variations in viewing angle, lighting, background clutter, and occlusion over multiple cameras. As a result of these variations, features d ..."
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Cited by 3 (3 self)
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Person re-identification in a non-overlapping multicamera scenario is an open challenge in computer vision because of the large changes in appearances caused by variations in viewing angle, lighting, background clutter, and occlusion over multiple cameras. As a result of these variations, features describing the same person get transformed between cameras. To model the transformation of features, the feature space is nonlinearly warped to get the “warp functions”. The warp functions between two instances of the same target form the set of feasible warp functions while those between instances of different targets form the set of infeasible warp functions. In this work, we build upon the observation that feature transformations between cameras lie in a nonlinear function space of all possible feature transformations. The space consisting of all the feasible and infeasible warp functions is the warp function space (WFS). We propose to learn a discriminating surface separating these two sets of warp functions in the WFS and to re-identify persons by classifying a test warp function as feasible or infeasible. Towards this objective, a Random Forest (RF) classifier is employed which effectively chooses the warp function components according to their importance in separating the feasible and the infeasible warp functions in the WFS. Extensive experiments on five datasets are carried out to show the superior performance of the proposed approach over state-of-the-art person re-identification methods. We show that our approach outperforms all other methods when large illumination variations are considered. At the same time it has been shown that our method reaches the best average performance over multiple combinations of the datasets, thus, showing that our method is not designed only to address a specific challenge posed by a particular dataset.
Appearance descriptors for person re-identification: a comprehensive review
- In: Proc. CoRR. (2013
"... Abstract. In video-surveillance, person re-identification 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 ..."
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Cited by 3 (0 self)
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Abstract. In video-surveillance, person re-identification 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 different body parts (e.g. torso and legs) independently. This paper provides a comprehensive review of current approaches to build appearance descriptors for person re-identification. The most relevant techniques are described in detail, and categorised according to the body models and features used. The aim of this work is to provide a structured body of knowledge and a starting point for researchers willing to conduct novel investigations on this challenging topic. 1
Scalable person re-identification: A benchmark
- In ICCV
, 2015
"... Abstract This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) ..."
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Cited by 2 (2 self)
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Abstract This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1)
Person re-identification: System design and evaluation overview
- In Person Re-Identification
, 2014
"... Abstract Person re-identification has important applications in video surveillance. It is particularly challenging because observed pedestrians undergo significant vari-ations across camera views, and there are a large number of pedestrians to be dis-tinguished given small pedestrian images from sur ..."
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Cited by 2 (1 self)
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Abstract Person re-identification has important applications in video surveillance. It is particularly challenging because observed pedestrians undergo significant vari-ations across camera views, and there are a large number of pedestrians to be dis-tinguished given small pedestrian images from surveillance videos. This chapter discusses different approaches of improving the key components of a person re-identification system, including feature design, feature learning and metric learning, as well as their strength and weakness. It provides an overview of various person re-identification systems and their evaluation on benchmark datasets. Mutliple bench-mark datasets for person re-identification are summarized and discussed. The per-formance of some state-of-the-art person identification approaches on benchmark datasets is compared and analyzed. It also discusses a few future research directions on improving benchmark datasets, evaluation methodology and system desgin. 1
Locality-constrained Collaborative Sparse Approximation for Multiple-shot Person Re-identification
"... Abstract—Person re-identification is becoming a hot research topic due to its academic importance and attractive applications in visual surveillance. This paper focuses on solving the relatively harder and more importance multiple-shot re-identification prob-lem. Following the idea of treating it as ..."
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Cited by 2 (1 self)
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Abstract—Person re-identification is becoming a hot research topic due to its academic importance and attractive applications in visual surveillance. This paper focuses on solving the relatively harder and more importance multiple-shot re-identification prob-lem. Following the idea of treating it as a set-based classification problem, we propose a new model called Locality-constrained Collaborative Sparse Approximation (LCSA) which is made to be as efficient, effective and robust as possible. It improves the very recently proposed Collaborative Sparse Approximation (CSA) model by introducing two types of locality constraints to enhance the quality of the data for collaborative approximation. Extensive experiments demonstrate that LCSA is not only much better than CSA in terms of effectiveness and robustness, but also superior to other related methods. I.
Joint dimension reduction and metric learning for person re-identification
- CoRR
"... Abstract. Person re-identification is an important technique towards automatic search of a person’s presence in a surveillance video. Among various methods developed for person re-identification, the Mahalanobis metric learning approaches have attracted much attention due to their impressive perform ..."
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Cited by 1 (0 self)
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Abstract. Person re-identification is an important technique towards automatic search of a person’s presence in a surveillance video. Among various methods developed for person re-identification, the Mahalanobis metric learning approaches have attracted much attention due to their impressive performance. In practice, many previous papers have applied the Principle Component Analysis (PCA) for dimension reduction be-fore metric learning. However, this may not be the optimal way for metric learning in low dimensional space. In this paper, we propose to jointly learn the discriminant low dimensional subspace and the dis-tance metric. This is achieved by learning a projection matrix and a Restricted Quadratic Discriminant Analysis (RQDA) model. We show that the problem can be formulated as a Generalized Rayleigh Quotient, and a closed-form solution can be obtained by the generalized eigen-value decomposition. We also present a practical computation method for RQDA, as well as its regularization. For the application of person re-identification, we propose a Retinex and maximum occurrence based feature representation method, which is robust to both illumination and viewpoint changes. Experiments on two challenging public databases, VIPeR and QMUL GRID, show that the performance of the proposed method is comparable to the state of the art.
