Effective Multiple Object Motion Detection Using Iterated Training Algorithm

Abstract

Motion detection has been done for videos with various methodologies. The existing systems are based on edge detection and detect motion as a single object by taking the movement of edges into account. But in sensitive applications like satellite imaging systems, cancer cell or medical imaging systems, the sub objects movement is also taken into account for efficient decision making. So a new methodology has been developed in this project for the multiple objects and sub objects movement in sensitive video applications. A methodology was developed for static images by Fellenszwalb et al for multiple object detection. The Iterated Training Algorithm (ITA) used for the static images is implied in the case of videos. This algorithm has been modified for the case of videos. In this paper ITA is implied in the case of videos and the sub objects movements in the video are detected. Webcam video is fed as input and the performance measure of sensitivity and numbers of frames detected with motion are visualized. It is found from the performance measures that, the proposed ITA holds better than the existing methods. Multiple Instance method had better performance than ITA but in the case of training, Multiple Instance method needs more training than the proposed method. As of whole, this paper validates the advantages of the proposed methodology.