Traffic Monitoring System, Target Tracking And Behavior Recognition

Intelligent traffic surveillance system with the characteristics of automatic and intelligent, has the ability of detecting traffic incidents, monitoring the pedestrians and vehicles in the traffic sence,can adapt to the needs of practical application. This paper focuses on object detection, object tracking, and object behavior analysis in the intelligent traffic surveillance system, then delves into the key problems in these three technologies,proposes new solutions. The paper works in the following aspects:(1) Most of current methods use a single model for the object detection exist many problems, such as high error rate, light sensitivity, poor robustness in the dynamic scenes,because of this,the paper presents a hybrid model of motion detection base on a certain blending rules, we integrate object detection model which is not sensitive to light changes and the other target detection model which can track scene changes quickly into a mixed-target detection model.The blending strategies are help for eliminating missed and false detections. Finally, the fast moving target detection method is used to reduce the computation of this model, together with the two models which have been integrated well both have simple computation procedure, the hybrid model still runs in real-time.(2) In the tracking, the paper mainly studies the object description in the tracking process, proposes a tracking algorithm base on multi-feature selection. We combinate RankBoost with AdaBoost to construct hybrid-boosting algorithm, then use hybrid-boost and the informations of target and background to selecte features,establish feature ranking classifiers, update feature ranking classifiers adaptivly in tracking time. Kalman filter is used to predict target area, then utilize Mean-shift algorithm combined with feature ranking classifiers to complete target tracking task precisly. The tracking algorithm above can select features adaptivly according to different objectives and background informations, it is very beneficial for overcoming illumination, interference, occlusion and so on in the traffic sence. (3) Present a motion behavior recognition method based on trajectory analysis. We use the cluster method to learn movement pattern of the trajectories, get the trajectory reference sequence which represent the campaign mode. Then trajectory is viewed as a time-varying data recording the target behavior, because of dynamic time warping (DTW) technique does not limit to the length of the time series,we combinate the DTW technology and K nearest neighbor algorithm to match the trajectory which will be identified with the reference trajectory sequence of the template.In the matching process, in order to accelerate the matching speed,using DTW lower bound function to exclude all non-similar trajectory after clustering,and then matching, identifing the target moving state.Experimental results show that the object detection, object tracking algorithm can detect objiect effectively and track object stable, the behavior recognition method based on trajectory analysis achieves a higher pedestrians behavior recognition rate at the intersection,for example turning left, turning right, going forward, U-type turns.