A General Video Surveillance Framework for Animal Behavior Analysis
This paper proposes a general intelligent video surveillance monitoring system to explore and examine some problems in animal behavior analysis particularly in cow behaviors. In this concern, farmers, animal health professionals and researchers have well recognized that analysis of changes in the behavioral patterns of cattle is an important factor for an animal health and welfare management system. Also, in today dairy world, farm sizes are growing larger and larger, as a result the attention time limits for individual animals smaller and smaller. Thus, video based monitoring system will become an emerging technology approaching to an era of intelligent monitoring system. In this context, image processing is a promising technique for such challenging system because it is relatively low cost and simple enough to implement. One of important issues in the management of group-housed livestock is to make early detection of abnormal behaviors of a cow. Particularly failure in detecting estrus in timely and accurate manner can be a serious factor in achieving efficient reproductive performance. Another aspect is concerned with health management to identify unhealthy or poor health such as lameness through analysis of measured motion data. Lameness is a one of the biggest health and welfare issue in modern intensive dairy farming. Although there has been a tremendous amount of methods for detecting estrus, still it needs to improve for achieving a more accurate and practical. Thus in this paper, a general intelligent video surveillance system framework for animal behavior analysis is proposed to be by using (i) various types of Background Models for target or targets extraction, (ii) Markov and Hidden Markov models for detection of various types of behaviors among the targets, (iii) Dynamic Programming and Markov Decision Making Process for producing output results. As an illustration, a pilot experiment will be performed to confirm the feasibility and validity of the proposed framework.