Performance Evaluation of Various Moving Object Segmentation Techniques for Intelligent Video Surveillance System

Moving object segmentation is an essential process for many computer vision algorithms. Many different methods have been proposed over the recent years but expert can be confused about their benefits and limitations. In this paper, review and comparative study of various moving object segmentation approachesis presented in terms of qualitative and quantitative performances with the aim of pointing out their strengths and weaknesses, and suggesting new research directions. For evaluation and analysis purposes, the various standard spatial domain methods include as proposed by McFarlane and Schofield [13], Kim et al [18], Oliver et al [27], Liu et al [9], Stauffer and Grimson's [15], Zivkovic [12], Lo and Velastin [25], Cucchiara et al. [26], Bradski [24], and Wren et al. [16]. For quantitative evaluation of these standard methods the various metrics used are RFAM (relative foreground area measure), MP (misclassification penalty), RPM (relative position based measure), and NCC (normalized cross correlation). The strengths and weaknesses of various segmentation approaches are discussed. From the results obtained, it is observed that codebook based segmentation method performs better in comparison to other methods in consideration.