Automatic Tracking and Labeling of Human Activities in a Video Sequence

This paper presents a novel approach for tracking multiple objects and a statistical learning approach for detection of human activities in a video sequence. For the tracking, a rigid transformation invariant appearance model combining color and edge information of the detected blob is proposed. For the activity detection, each activity label is regarded as a hypothesis. Given some labeled sequences, a group of features are first extracted from motion trajectories of each detected object and the likelihood of each feature under that hypothesis is calculated. A dynamic programming-based training algorithm is applied to get an optimal classifier for each feature. Then it selects the classifiers with the most discriminative power and combines them to form a stronger classifier. This algorithm complies with criterion so that it is guaranteed to achieve a specified detection rate as well as a minimized false alarm rate. Results on dataset 1show the effectiveness of the proposed algorithm.

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