This paper presents an automatic system for detecting and re-identifying people moving in different sites with non-overlapping views. We first propose an automatic process for silhouette extraction based on the combination of an adaptive background subtraction algorithm and a motion detection module. Such a combination takes advantage of both approaches and is able to tackle the problem of particular environments. The silhouette extraction results are then clustered based on their spatial belonging and colorimetric characteristics in order to preserve only the key regions that effectively represent the appearance of a person. The next important step consists in characterizing the extracted silhouettes by the appearance-based signatures. Our proposed descriptor, which includes both color and spatial feature of objects, leads to satisfying results compared to other descriptors in the literature. Since the passage of a person needs to be characterized by multiple frames, a large quantity of data has to be processed. Thus, a graph-based algorithm is used to realize the comparison of passages of people in front of cameras and to make the final decision of re-identification. The global system is tested on two real and difficult data sets recorded in very different environments. The experimental results show that our proposed system leads to very satisfactory results.
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Dung-Nghi TRUONG CONG, Louahdi KHOUDOUR, Catherine ACHARD, Lounis DOUADI, "People Detection and Re-Identification in Complex Environments" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 7, pp. 1761-1772, July 2010, doi: 10.1587/transinf.E93.D.1761.
Abstract: This paper presents an automatic system for detecting and re-identifying people moving in different sites with non-overlapping views. We first propose an automatic process for silhouette extraction based on the combination of an adaptive background subtraction algorithm and a motion detection module. Such a combination takes advantage of both approaches and is able to tackle the problem of particular environments. The silhouette extraction results are then clustered based on their spatial belonging and colorimetric characteristics in order to preserve only the key regions that effectively represent the appearance of a person. The next important step consists in characterizing the extracted silhouettes by the appearance-based signatures. Our proposed descriptor, which includes both color and spatial feature of objects, leads to satisfying results compared to other descriptors in the literature. Since the passage of a person needs to be characterized by multiple frames, a large quantity of data has to be processed. Thus, a graph-based algorithm is used to realize the comparison of passages of people in front of cameras and to make the final decision of re-identification. The global system is tested on two real and difficult data sets recorded in very different environments. The experimental results show that our proposed system leads to very satisfactory results.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.1761/_p
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@ARTICLE{e93-d_7_1761,
author={Dung-Nghi TRUONG CONG, Louahdi KHOUDOUR, Catherine ACHARD, Lounis DOUADI, },
journal={IEICE TRANSACTIONS on Information},
title={People Detection and Re-Identification in Complex Environments},
year={2010},
volume={E93-D},
number={7},
pages={1761-1772},
abstract={This paper presents an automatic system for detecting and re-identifying people moving in different sites with non-overlapping views. We first propose an automatic process for silhouette extraction based on the combination of an adaptive background subtraction algorithm and a motion detection module. Such a combination takes advantage of both approaches and is able to tackle the problem of particular environments. The silhouette extraction results are then clustered based on their spatial belonging and colorimetric characteristics in order to preserve only the key regions that effectively represent the appearance of a person. The next important step consists in characterizing the extracted silhouettes by the appearance-based signatures. Our proposed descriptor, which includes both color and spatial feature of objects, leads to satisfying results compared to other descriptors in the literature. Since the passage of a person needs to be characterized by multiple frames, a large quantity of data has to be processed. Thus, a graph-based algorithm is used to realize the comparison of passages of people in front of cameras and to make the final decision of re-identification. The global system is tested on two real and difficult data sets recorded in very different environments. The experimental results show that our proposed system leads to very satisfactory results.},
keywords={},
doi={10.1587/transinf.E93.D.1761},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - People Detection and Re-Identification in Complex Environments
T2 - IEICE TRANSACTIONS on Information
SP - 1761
EP - 1772
AU - Dung-Nghi TRUONG CONG
AU - Louahdi KHOUDOUR
AU - Catherine ACHARD
AU - Lounis DOUADI
PY - 2010
DO - 10.1587/transinf.E93.D.1761
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2010
AB - This paper presents an automatic system for detecting and re-identifying people moving in different sites with non-overlapping views. We first propose an automatic process for silhouette extraction based on the combination of an adaptive background subtraction algorithm and a motion detection module. Such a combination takes advantage of both approaches and is able to tackle the problem of particular environments. The silhouette extraction results are then clustered based on their spatial belonging and colorimetric characteristics in order to preserve only the key regions that effectively represent the appearance of a person. The next important step consists in characterizing the extracted silhouettes by the appearance-based signatures. Our proposed descriptor, which includes both color and spatial feature of objects, leads to satisfying results compared to other descriptors in the literature. Since the passage of a person needs to be characterized by multiple frames, a large quantity of data has to be processed. Thus, a graph-based algorithm is used to realize the comparison of passages of people in front of cameras and to make the final decision of re-identification. The global system is tested on two real and difficult data sets recorded in very different environments. The experimental results show that our proposed system leads to very satisfactory results.
ER -