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19
Semi-supervised multifeature learning for person re-identification
- In IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS
, 2013
"... Abstract Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically design ..."
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Cited by 11 (7 self)
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Abstract Person re-identification is probably the open challenge for low-level video surveillance in the presence of a camera network with non-overlapped fields of view. A large number of direct approaches has emerged in the last five years, often proposing novel visual features specifically designed to highlight the most discriminant aspects of people, which are invariant to pose, scale and illumination. On the other hand, learning-based methods are usually based on simpler features, and are trained on pairs of cameras to discriminate between individuals. In this paper, we present a method that joins these two ideas: given an arbitrary stateof-the-art set of features, no matter their number, dimensionality or descriptor, the proposed multi-class learning approach learns how to fuse them, ensuring that the features agree on the classification result. The approach consists of a semi-supervised multi-feature learning strategy, that requires at least a single image per person as training data. To validate our approach, we present results on different datasets, using several heterogeneous features, that set a new level of performance in the person re-identification problem.
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 WITH A PTZ CAMERA: AN INTRODUCTORY STUDY
"... RGB-Did iLIDS-MA We present an introductory study that paves the way for a new kind of person re-identification, by exploiting a single Pan-Tilt-Zoom (PTZ) camera. PTZ devices allow to zoom on body regions, acquiring discriminative visual patterns that enrich the appearance description of an individ ..."
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Cited by 4 (2 self)
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RGB-Did iLIDS-MA We present an introductory study that paves the way for a new kind of person re-identification, by exploiting a single Pan-Tilt-Zoom (PTZ) camera. PTZ devices allow to zoom on body regions, acquiring discriminative visual patterns that enrich the appearance description of an individual. This intuition has been translated into a statistical direct reidentification scheme, which collects two images for each probe subject: the first image captures the probe individual, focusing on the whole body; the second can be a zoomed body part (head, torso or legs) or another whole body image, and is the outcome of an action-selection mechanism, driven by feature selection principles. The validation of this technique is also explored: in order to allow repeatability, two novel multi-resolution benchmarks have been created. On these data, we demonstrate that our approach selects effective actions, by focusing on body portions which discriminate each subject. Moreover, we show that the proposed compound of two images overwhelms standard multi-shot descriptions, composed by many more pictures. Index Terms — Person Re-identification, Pan-Tilt-Zoom camera 1.
MULTI-TARGET TRACKING BY DISCRIMINATIVE ANALYSIS ON RIEMANNIAN MANIFOLD
, 2012
"... This paper addresses the problem of multi-target tracking in crowded scenes from a single camera. We propose an algorithm for learning discriminative appearance models for different targets. These appearance models are based on covariance descriptor extracted from tracklets given by a short-term tra ..."
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Cited by 4 (1 self)
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This paper addresses the problem of multi-target tracking in crowded scenes from a single camera. We propose an algorithm for learning discriminative appearance models for different targets. These appearance models are based on covariance descriptor extracted from tracklets given by a short-term tracking algorithm. Short-term tracking relies on object descriptors tuned by a controller which copes with context variation over time. We link tracklets by using discriminative analysis on a Riemannian manifold. Our evaluation shows that by applying this discriminative analysis, we can reduce false alarms and identity switches, not only for tracking in a single camera but also for matching object appearances between non-overlapping cameras. Index Terms — tracking, controller, re-identification, covariance matrix
Saliency weighted features for person re-identification
- in Proc. ECCV
"... Abstract. In this work we propose a novel person re-identification ap-proach. The solution, inspired by human gazing capabilities, wants to identify the salient regions of a given person. Such regions are used as a weighting tool in the image feature extraction process. Then, such novel representati ..."
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Cited by 4 (2 self)
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Abstract. In this work we propose a novel person re-identification ap-proach. The solution, inspired by human gazing capabilities, wants to identify the salient regions of a given person. Such regions are used as a weighting tool in the image feature extraction process. Then, such novel representation is combined with a set of other visual features in a pairwise-based multiple metric learning framework. Finally, the learned metrics are fused to get the distance between image pairs and to re-identify a person. The proposed method is evaluated on three different benchmark datasets and compared with best state-of-the-art approaches to show its overall superior performance. 1
Remote Feature Learning for Mobile Re-Identification
- In International conference on Distributed Smart Cameras, Palm
, 2013
"... Abstract—This work introduces a novel method for person re-identification using embedded smart cameras. State-of-the-art methods address the re-identification problem using global and local features, metric learning and feature transformation algorithms. Such methods require advanced systems with hi ..."
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Cited by 4 (3 self)
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Abstract—This work introduces a novel method for person re-identification using embedded smart cameras. State-of-the-art methods address the re-identification problem using global and local features, metric learning and feature transformation algorithms. Such methods require advanced systems with high computational capabilities. Nowadays, there is a growing interest in security applications using embedded cameras. Motivated by this we propose to study a new system that addresses the challenges posed by the re-identification problem using devices (e.g. smartphones, etc.) that have limited resources. In this work we introduce a novel client-server system that exploits a feature learning method to achieve a two-fold objective: (i) maximize the re-identification performance over time and (ii) reduce the required computational costs. In the training phase, state-of-the-art features are selected considering both the device capabilities and re-identification performance. During the detection phase, the re-identification performance are maximized by selecting the best features for a given input image. To demonstrate the performance of the proposed method we conduct the experiments using different mobile devices. Statistics about feature extraction and feature matching are presented together with re-identification results. I.
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.
Multi-Shot Re-Identification with Random-Projection-Based Random Forests
"... Human re-identification remains one of the fundamen-tal, difficult problems in video surveillance and analysis. Current metric learning algorithms mainly focus on find-ing an optimized vector space such that observations of the same person in this space have a smaller distance than ob-servations of ..."
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Cited by 3 (3 self)
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Human re-identification remains one of the fundamen-tal, difficult problems in video surveillance and analysis. Current metric learning algorithms mainly focus on find-ing an optimized vector space such that observations of the same person in this space have a smaller distance than ob-servations of two different people. In this paper, we pro-pose a novel metric learning approach to the human re-identification problem, with an emphasis on the multi-shot scenario. First, we perform dimensionality reduction on image feature vectors through random projection. Next, a random forest is trained based on pairwise constraints in the projected subspace. This procedure repeats with a num-ber of random projection bases, so that a series of random forests are trained in various feature subspaces. Finally, we select personalized random forests for each subject using their multi-shot appearances. We evaluate the performance of our algorithm on three benchmark datasets. 1.
Person orientation and feature distances boost re-identification
- In ICPR
, 2014
"... Abstract—Most of the open challenges in person re-identification arise from the large variations of human ap-pearance and from the different camera views that may be involved, making pure feature matching an unreliable solution. To tackle these challenges state-of-the-art methods assume that a uniqu ..."
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Cited by 2 (1 self)
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Abstract—Most of the open challenges in person re-identification arise from the large variations of human ap-pearance and from the different camera views that may be involved, making pure feature matching an unreliable solution. To tackle these challenges state-of-the-art methods assume that a unique inter-camera transformation of features undergoes between two cameras. However, the combination of view points, scene illumination and photometric settings, etc., together with the appearance, pose and orientation of a person make the inter-camera transformation of features multi-modal. To address these challenges we introduce three main contributions. We propose a method to extract multiple frames of the same person with differ-ent orientation. We learn the pairwise feature dissimilarities space (PFDS) formed by the subspace of pairwise feature dissimilarities computed between images of persons with similar orientation and the subspace of pairwise feature dissimilarities computed between images of persons non-similar orientations. Finally, a classifier is trained to capture the multi-modal inter-camera transformation of pairwise images for each subspace. To validate the proposed approach we show the superior performance of our approach to state-of-the-art methods using two publicly available benchmark datasets. I.
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.
