It has become increasingly popular to study animal behaviors with the assistance of video recordings. The traditional manual human video annotation is a time and labor consuming process and, the observation results vary between different observers. Hence an automated video processing and behavior analysis system is desirable. We propose a framework for automatic video based behavior analysis systems, which consists of four major modules: behavior modeling, feature extraction from video sequences, basic behavior unit (BBU) discovery and complex behavior recognition. In this paper, we focus on BBU discovery using the affinity graph method on the feature data extracted from video images. We present a simple yet effective way of fusing information from multiple cameras in BBU discovery, and we present and analyze results on artificial mouse video using single, stereo and three cameras. Overall the results are encouraging.
['Xinwei Xue', 'Thomas C. Henderson']
Video-based Animal Behavior Analysis From Multiple Cameras