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.
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.
Vision algorithms face many challenging issues when it comes to analyze human activities in video surveillance applications. For instance, occlusions makes the detection and tracking of people a hard task to perform. Hence advanced and adapted solutions are required to analyze the content of video sequences. We here present a people detection algorithm based on a hierarchical tree of Histogram of Oriented Gradients referred to as HOG. The detection is coupled with independently trained body part detectors to enhance the detection performance and to reach state of the art performances. We adopt a person tracking scheme which calculates HOG dissimilarities between detected persons throughout a sequence. The algorithms are tested in videos with challenging situations such as occlusions. False alarms are further reduced by using 2D and 3D information of moving objects segmented from a background reference frame.
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