Moving Object Detection using Tracking, Background Subtraction and Identifying Outliers in Low Rank Video

Detection of moving objects in a video sequence is a difficult task and robust moving object detection in video frames for video surveillance applications is a challenging problem. Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Frequently, an object detector requires manual labeling, while background subtraction needs a training sequence. To automate the analysis, object detection without a separate training phase becomes a critical task. We done a survey of various techniques related to moving object detection and propose the optimization methods that can lead to improved object detection and the speed of formulating the low rank model for detected object. In this project proposes, the three modules for detecting moving object with fixed camera and detecting moving object with moving camera and detecting and removing outlier present in sequence of frames, so we consider the outlier may be any variation, distortion or noise in the sequence of frames. The project proposes the modules, work on first process video then segment video and robustly recognized the moving object in video sequence and removing the outlier with low rank model.