Direction-based stochastic matching for pedestrian recognition in non-overlapping cameras

• Published in: 2011 18th IEEE International Conference on Image Processing pp. 2065 - 2068
• Main Authors: Xiaotang Chen, Kaiqi Huang, Tieniu Tan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2011
• Subjects: Cameras; Conferences; Directional cues; Feature extraction; Indexes; Non-overlapping camera views; Pedestrian recognition; Robustness; Stochastic matching; Training
• ISBN: 1457713047; 9781457713040
• ISSN: 1522-4880; 2381-8549
• DOI: 10.1109/ICIP.2011.6115887

Pedestrian recognition is a challenging problem in non-overlapping multi-camera object tracking. In this paper, we present a novel approach for matching pedestrians across non-overlapping multiple cameras without the need of a training phase or spatio-temporal cues across cameras. To deal with viewpoint changes, we introduce the concept of directional angles estimated using the spatio-temporal continuity in the single camera tracking. To deal with pose changes, a stochastic matching strategy is performed, where the similarity of two blobs belonging to different viewpoints is calculated by a novel similarity measurement algorithm. The experiments are performed on different multi-view datasets. Experimental results demonstrate the effectiveness and robustness of the proposed method.