Occluded Pedestrian Tracking using Collaboration of Kalman Filter and Particle Filter

• Main Authors: Yen-Hsiang Chang, 張雁翔
• Other Authors: Daw-Tung Lin
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/79092032066548672333

碩士 === 國立臺北大學 === 通訊工程研究所 === 98 === Object tracking is a one of the key feature in intelligent video surveillance. It is a challenging task in tracking algorithm due to the frequent occlusion encountered between moving objects. We propose a novel method to address the problem of tracking and evaluating the number of people in multiple people scenes with an occlusion condition. The proposed method combines an object tracking system and a head detection. In our framework, Kalman Filter and Particle Filter provide robust object tracking for solving the occlusion between moving object. The head detection adopts the color model and shape-based object detection for counting the number of people. Extensive experimental results show that our method possesses effective and efficient performance.