People re-detection using Adaboost with sift and color correlogram

People re-detection aims at performing re-identification of people who leave the scene and reappear after some time. This is an important problem especially in video surveillance scenarios. In this paper, we present a method of people re-detection within the context of visual sequence in single-camera setup. We consider re-detection as a binary classification problem, where both global and local descriptors are employed for training strong classifier on-line with Adaboost to distinguish a newly detected people as tracked or new occurrence. The strong classifier will be updated while match is ascertained. A predetermined classifier with well-chosen threshold is employed as assistant of training examples collection. We test the performance of our approach on 4 different scenes including 51 video sequences taken from the CAVIAR database and 4 video sequences shot by ourselves. The results show that our re-detection algorithm can robustly handle variations in illumination, pose, scale, an...

• Type: Conference
• Year: 2008
• Where: ICIP
• Authors: Lei Hu, Shuqiang Jiang, Qingming Huang, Wen Gao
• Keywords: Binary Classification Problem, ICIP 2008, Image Processing, Strong Classifier, Video Sequences, Video Surveillance Scenarios, Well-chosen Threshold