Loitering detection using an associating pedestrian tracker in crowded scenes.

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Bibliographic Details
Title:	Loitering detection using an associating pedestrian tracker in crowded scenes.
Authors:	Nam, Yunyoung¹
Source:	Multimedia Tools & Applications. May2015, Vol. 74 Issue 9, p2939-2961. 23p.
Subjects:	Object tracking (Computer vision), Loitering, Closed-circuit television, Television in security systems, Spatiotemporal processes
Abstract:	This paper presents a loitering detection method using an associating pedestrian tracker in public areas. We analyze the spatio-temporal characteristics to perform monitoring of people and generate alerts when loitering persons are detected. To determine and adjust a time threshold for raising an alarm, we obtain the mean time of stay for normal and abnormal situation. In addition, we consider an optimal threshold for staying time and escaping time to deal with various conditions. For object identification, we measure the mean square error and histogram of oriented gradients. In order to trace moving objects continuously, the HSI color model and a combination of Euclidean distance, color difference, and shape difference are measured based on consistent labeling tracking. To evaluate the performance of our method, we showed detection results of the PETS2007 dataset using thresholds obtained by our proposed methods. Our experiments show promising results with 75.45 % averaged recall rate and 87.12 % averaged precision rate were obtained in loitering objects. We also compared the proposed method to other reported methods. The experimental results showed a significant improvement on precision. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This paper presents a loitering detection method using an associating pedestrian tracker in public areas. We analyze the spatio-temporal characteristics to perform monitoring of people and generate alerts when loitering persons are detected. To determine and adjust a time threshold for raising an alarm, we obtain the mean time of stay for normal and abnormal situation. In addition, we consider an optimal threshold for staying time and escaping time to deal with various conditions. For object identification, we measure the mean square error and histogram of oriented gradients. In order to trace moving objects continuously, the HSI color model and a combination of Euclidean distance, color difference, and shape difference are measured based on consistent labeling tracking. To evaluate the performance of our method, we showed detection results of the PETS2007 dataset using thresholds obtained by our proposed methods. Our experiments show promising results with 75.45 % averaged recall rate and 87.12 % averaged precision rate were obtained in loitering objects. We also compared the proposed method to other reported methods. The experimental results showed a significant improvement on precision. [ABSTRACT FROM AUTHOR]
ISSN:	13807501
DOI:	10.1007/s11042-013-1763-7