Adaptive synopsis of non-human primates’ surveillance video based on behavior classification

Abstract

Non-human primates (NHPs) play a critical role in biomedical research. Automated monitoring and analysis of NHP’s behaviors through the surveillance video can greatly support the NHP-related studies. However, little research work has been undertaken yet. There are two challenges in analyzing the NHP’s surveillance video: the NHP’s behaviors are lack of regularity and intention, and serious occlusions are brought by the fences of the cages. In this paper, four typical NHPs’ behaviors are defined based on the requirement in pharmaceutical analysis. We design a novel feature set combining contextual attributes and local motion information to overcome the effects of occlusions. A hierarchical linear discriminant analysis (LDA) classifier is proposed to categorize the NHPs’ behaviors. Based on the behavior classification, an adaptive synopsis algorithm is further proposed to condense the NHPs’ surveillance video, which offers a mechanism to retrieve any NHP’s behavior information corresponding to specified events or time periods in the surveillance video. Experimental results show the effectiveness of the proposed method in categorizing and condensing NHPs’ surveillance video.