31-01-2013, 04:34 PM
A Survey on Moving Object Detection and Tracking in Video Surveillance System
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Abstract
This paper presents a survey of various techniques related to video surveillance system improving the security. The goal of this paper is to review of various moving object detection and object tracking methods. This paper focuses on detection of moving objects in video surveillance system then tracking the detected objects in the scene. Moving Object detection is first low level important task for any video surveillance application. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey, I described Background subtraction with alpha, statistical method, Eigen background Subtraction and Temporal frame differencing to detect moving object. I also described tracking method based on point tracking, kernel tracking and silhouette tracking.
INTRODUCTION OF VIDEO SURVEILLANCE
Video surveillance is a process of analyzing video sequences. It is an active area in computer vision. It gives huge amount of data storage and display. There are three types of Video surveillance activities. Video surveillance activities can be manual, semi-autonomous or fully-autonomous [10]. Manual video surveillance involves analysis of the video content by a human. Such systems are currently widely used. Semi-autonomous video surveillance involves some form of video processing but with significant human intervention. Typical examples are systems that perform simple motion detection [5]. Only in the presence of significant motion the video is recorded and sent for analysis by a human expert. By a fully-autonomous system [10], only input is the video sequence taken at the scene where surveillance is performed. In such a system there is no human intervention and the system does both the low-level tasks, like motion detection and tracking, and also high-level decision making tasks like abnormal event detection and gesture recognition. Video surveillance system that supports automated objects classification and object tracking. Monitoring of video for long duration by human operator is impractical and infeasible. Automatic motion detection which can provide batter human attention [9]
MOVING OBJECT DETECTION
Moving object detection is the basic step for further analysis of video. Every tracking method requires an object detection mechanism either in every frame or when the object first appears in the video. It handles segmentation of moving objects from stationary background objects [3]. This focuses on higher level processing .It also decreases computation time. Due to environmental conditions like illumination changes, shadow object segmentation becomes difficult and significant problem. A common approach for object detection is to use information in a single frame. However, some object detection methods make use of the temporal information computed from a sequence of frames to reduce the number of false detections [16]. This temporal information is usually in the form of frame differencing, which highlights regions that changes dynamically in consecutive frames. Given the object regions in the image, it is then the tracker’s task to perform object correspondence from one frame to the next to generate the tracks. This section reviews three moving object detection methods that are background subtraction with alpha parameter, temporal difference, and statistical methods, Eigen Background Subtraction.
Foreground Detection
The main purpose of foreground detection is to distinguishing foreground objects from the stationary background. Almost, each of the video surveillance systems uses the first step is detecting foreground objects. This creates a focus of attention for higher processing levels such as tracking, classification and behavior understanding and reduces computation time considerably since only pixels belonging to foreground objects need to be dealt with [1]. The first step is the background scene initialization. There are various techniques used to model the background scene. The background scene related parts of the system is isolated and its coupling with other modules is kept minimum to let the whole detection system to work flexibly with any one of the background models [8].
Background Subtraction with Alpha
Object detection can be achieved by building a representation of the scene called the background model and then finding deviations from the model for each incoming frame. Any significant change in an image region from the background model signifies a moving object. The pixels constituting the regions undergoing change are marked for further processing. Usually, a connected component algorithm is applied to obtain connected regions corresponding to the objects. This process is referred to as the background subtraction [6].
Heikkila and Silven [6] presented this technique. At the start of the system reference background is initialized with first few frames of video frame and that are updated to adapt dynamic changes in the scene. At each new frame foreground pixels are detected by subtracting intensity values from background and filtering absolute value of differences with dynamic threshold per pixel [8] .The threshold and reference background are updated using foreground pixel information. It attempts to detect moving regions by subtracting the current image pixel-by-pixel from a reference background image that is created by averaging images over time in an initialized period [6]. The pixels where the difference is above a threshold are classified as foreground. After creating foreground pixel map, some morphological post processing operations such as erosion, dilation and closing are performed to reduce the effects of noise and enhance the detected regions. The reference background is updated with new images over time to adapt to dynamic scene changes.
CONCLUSION
To analyze images and extract high level information,
image enhancement, motion detection, object tracking and
behavior understanding researches have been studied. In this
paper, we have studied and presented different methods of
moving object detection, used in video surveillance. We have
described background subtraction with alpha, temporal differencing, statistical methods. Detection techniques into various categories, here, we also discuss the related issues, to the moving object detection technique. The drawback of temporal differencing is that it fails to extract all relevant pixels of a foreground object especially when the object has uniform texture or moves slowly. When a foreground object stops moving, temporal differencing method fails in detecting a change between consecutive frames and loses the track of the object. We presented detail of background subtraction method in deep because of its computational effectiveness and accuracy. This article gives valuable insight into this important research topic and encourages the new research in the area of moving object detection as well as in the field of computer vision.