Person Re-identification Using Spatial Covariance Regions of Human Body Parts

7th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS - 2010 08/2010;
Source: OAI


In many surveillance systems there is a requirement to determine whether a given person of interest has already been observed over a network of cameras. This is the person re-identification problem. The human appearance obtained in one camera is usually different from the ones obtained in another camera. In order to re-identify people the human signature should handle difference in illumination, pose and camera parameters. We propose a new appearance model based on spatial covariance regions extracted from human body parts. The new spatial pyramid scheme is applied to capture the correlation between human body parts in order to obtain a discriminative human signature. The human body parts are automatically detected using Histograms of Oriented Gradients (HOG). The method is evaluated using benchmark video sequences from i-LIDS Multiple-Camera Tracking Scenario data set. The re-identification performance is presented using the cumulative matching characteristic (CMC) curve. Finally, we show that the proposed approach outperforms state of the art methods.

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Available from: Sławomir Bąk, Jan 15, 2015
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    • "As we see from figure 8, performance of DCD is far behind performance of the other approaches. This is due to the strong environment and camera type induced color differences between the cameras, which are not (see [1]) resolvable by color calibration between cameras. Performance of ISM re-identification is comparable with Haar performance. "
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    ABSTRACT: In this paper, we present an approach for person re-identification in multi-camera networks. This approach employs the Implicit Shape Model and SIFT features for person re-identification. One important property of the re-identification approach is that it is closely coupled to a person detection and tracking and uses SIFT feature models which are built during the tracking. We hold this coupling to be an important point because re-identification depends on models that are to be acquired during tracking. These models are then used to re-identify a person when it reappears in the system's field of view. Re-identification itself is performed in a 3-staged approach which allows for efficient re-identification and is perfectly suited for distributed processing where bandwidth concerns are relevant. We show that this re-identification approach which was formerly only evaluated for single camera person re-identification can be successfully applied to the task of multi-camera re-identification. Evaluation in a challenging real-world multi-camera scenario shows that the generic approach which does not use color or other sensor specific features and thus is applicable independently of such sensor specifics shows performance at least comparable to specialized state-of-the-art approaches.
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on; 06/2011
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    • "(ii) The multi-stage approach with increasing computational cost allows for very efficient re-identification since the computational cheap first stages can be used to reduce the amount of data (candidate models from the database) that has to be considered in the last stage. (iii) Compared to most other state-of-the-art approaches like [3] [4], this approach is applicable in real applications since it is integrated with a detection and tracking strategy. Specifically, it does not rely on manual annotation of people like [3, 4] and builds models for re-identification online without an offline training step like [2] [5]. "
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    ABSTRACT: In this paper, we approach the task of appearance based person re-identification for scenarios where no biometric features can be used. For that, we build on a person re-identification approach that uses the Implicit Shape Model (ISM) and SIFT features for re-identification. This approach builds identity models of persons during tracking and employs these models for re-identification. We apply this re-identification, which was until now only evaluated in the infrared spectrum, to data acquired in the visible spectrum. Furthermore we evaluate view independence of the re-identification approach and introduce methods that extend view invariance. Specifically, we (i) propose a method for online view-determination of a tracked person, (ii) use the online view-determination to generate view specific identity models of persons which increase model distinctiveness in re-identification, and (iii) introduce a method to convert identity models between views to increase view independence.
    Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on; 01/2011
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    ABSTRACT: Recent advances in visual tracking methods allow following a given object or individual in presence of significant clutter or partial occlusions in a single or a set of overlapping camera views. 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. Methods that effectively address the challenges associated with changes in illumination, pose, and clothing appearance variation are discussed. More specifically, the development of a set of models that capture the overall appearance of an individual and can effectively be used for information retrieval are reviewed. Some of them provide a holistic description of a person, and some others require an intermediate step where specific body parts need to be identified. Some are designed to extract appearance features over time, and some others can operate reliably also on single images. The paper discusses algorithms for speeding up the computation of signatures. In particular it describes very fast procedures for computing co-occurrence matrices by leveraging a generalization of the integral representation of images. The algorithms are deployed and tested in a camera network comprising of three cameras with non-overlapping field of views, where a multi-camera multi-target tracker links the tracks in different cameras by reidentifying the same people appearing in different views.
    Journal of Ambient Intelligence and Humanized Computing 06/2011; 2(2):127-151. DOI:10.1007/s12652-010-0034-y
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