Research On Method Of Robust Intelligent Visual Survellance

Intelligent visual surveillance is a new research area in computer vision. It is quite different from the traditional surveillance systems that it not only replaces the human eyes with camera, but also builds an automatic or semi-automatic video understanding and analysis system in the use of computer software. It can offer accurate analysis result, and announce an alarm for abnormal behaviors. Its mainly research topics include moving object detection, moving object recognition and tracking, abnormal behavior detection and alarm. It can be applied in several areas such as transportation, public security, custom, bank and military affairs.Many famous institution and researchers have shown their interest in intelligent visual surveillance system, like Carnegie Mellon university and CASIA. As a matter of fact, they have built the surveillance platform for it. In order to improve the reliability and intelligence, several key problems should be conquered. In this paper, we briefly researched the robust intelligent visual surveillance methods, and brought out a robust solution which can handle object detection and tracking in bad weather conditions, including daytime, nighttime and foggy day situation, and can also take care the affection of moving shadow and occlusion. We solved several key problems such as image restoration method in foggy day, moving object detection in nighttime, moving shadow detection and tracking with object occlusion. Our meaningful and detailed research work is organized as follows:(1) An Unscented Kalman Filter(UKF) based background subtraction method is proposed, and a whole moving object detection frame is constructed. Background is modeled firstly. Then the dynamic change of pixels is analyzed through two levels which are frame to frame differencing and background differencing. Finally, UKF is used to update the model parameters online, and realize real-time moving object segmentation.(2) Scene visibility is very low in foggy day. We proposed a novel physical model based defog method to make sure the intelligent visual surveillance system work normally. This method models foggy day scene points firstly. Then scene depth is calculated with one clear day image and one foggy day image. The foggy day image or video is restored using depth information finally.(3) According to the condition of nighttime environment, we proposed two novel methods for nighttime video enhancement and moving object detection. They can enhance the original low quality nighttime image, and the experimental results of the moving object detection show that our methods are effective and satisfying. Moreover, the final fused daytime and nighttime image contains a comprehensive description of the scene which is more useful for human vision and machine perception.(4) Moving shadows would connect moving objects together, and affect object recognition and tracking. From the feature of moving shadows, we proposed two novel methods to handle this situation, including improved feature based method, and edge feature and corner point information based method. Applied to different surveillance scenes, our methods can detect and remove moving shadows well.(5) In order to solve the occlusion problem in object tracking, a movement estimation frame based tracking method is proposed, and combined with parallelogram contour based occlusion segmentation method to realize a robust real-time multi-vehicle tracking.