Research And Implementation Of Abandoned Object Detection In Video Surveillance

With the development of society and economy, people’s security needs is increasing. Terrorists usually commit attacks in followed places, Such as airport, subway, and waiting hall and so on. So a real time monitored system is needed. But traditional monitoring system can only be used for the basic evidence and not capable of real-time detecting abnormal events, so people look forward to a kind of intelligent technology for security. So the intelligent video surveillance system is emerging.The main study in this paper is detecting abandoned object in surveillance video. So far the abandoned event detection is based on target detection and target tracking, but they are not robust sometimes. For example, the method which based on target detection can do false alarm, due to the initial background scene has the bigger weight in slow update rate background model and if has some move target, the area of the move target can’t update in time, so the area easily be judged as abandoned object area. And when somebody enter in scene and keep still during a period time, the algorithm will judge the person as abandoned object. In background update stage, foreground object sometimes will quickly melt into the background; if the background learning rate is not good,"ghost" may be happening. Abandoned object detection which based on tracking method, mainly focus on the moving target position prediction and identification, the method easily affected by light and other factors, human body as non-rigid objects in tracking is difficult to use a template to detect and track, often leads to inaccurate prediction, so that target will lose sometimes.In this paper, first of all, I do amount of research and reviewed on the basis of related algorithms, and then analysis the principle and difficulty, made some improvements and creation based on the existing achievement. The works and creation of this paper are as follows:Firstly, review the existing detection algorithm, do analysis on detecting principle, key points, difficulty and the step of implementation. Secondly, the main idea of this paper is based on detection method. At the beginning detect temporarily stationary target using the dual-background model, they are slow update moving average background model and fast update Mixture Gauss background model. And then, using Blob tracks every target which detected at beginning. Do analysis on target edge and center-peripheral region histogram, discriminate the target whether abandoned or not.Thirdly, human detection based on HOG (Histogram of Oriented Gradient) joint in proposed algorithm can detect human body and eliminate misjudgment caused by human keep still in scene.Finally, implement the detection system. Through testing the video which are from the online standard video lib and captured by ourselves, the system and the algorithm have achieved good results.