Feature fusion for basic behavior unit segmentation from video sequences

It has become increasingly popular to study animal behaviors with the assistance of video recordings. An automated video processing and behavior analysis system is desired to replace the traditional manual annotation. 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. BBU discovery is performed based on features extracted from video sequences, hence the fusion of multiple dimensional features is very important. In this paper, we explore the application of feature fusion techniques to BBU discovery with one and multiple cameras. We applied the vector fusion (SBP) method, a multi-variate vector visualization technique, in fusing the features obtained from a single camera. This technique reduces the multiple dimensional data into two dimensional (SBP) space, and the spatial and temporal analysis in SBP space can help discover the underlying data groups. Then we present a simple feature fusion technique for BBU discovery from multiple cameras with the affinity graph method. Finally, we present encouraging results on a physical system and a synthetic mouse-in-a-cage scenario from one, two, and three cameras. The feature fusion methods in this paper are simple yet effective.