Article

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

7th IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS - 2010 01/2010; DOI: 10.1109/AVSS.2010.34
Source: OAI

ABSTRACT 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.

0 Bookmarks
 · 
166 Views
  • Source
    [Show abstract] [Hide abstract]
    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 reidentification approach that uses the Implicit Shape Model (ISM) and SIFT features for re-identification. This approach
    Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on; 01/2011
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Human re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. We propose a new appearance model combining information from multiple images to obtain highly discriminative human signature, called Mean Riemannian Covariance Grid (MRCG). The method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two other more pertinent datasets.
    Advanced Video and Signal-Based Surveillance (AVSS), 2011 8th IEEE International Conference on; 10/2011
  • Source
    [Show abstract] [Hide abstract]
    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 01/2011; 2:127-151.

Full-text

View
0 Downloads
Available from