Research On Methods For Traffic Event Recognition Based On Video Processing

In recent years, video detection technology is applied more and more in the field of intelligent transportation. Traffic event recognition based on video is one of the most promising applications in it. Traffic event automatic recognition technology could provide basic information for urban intelligent transportation management and control, and play an important role in easing traffic jams, reducing traffic accidents and ensuring travel safety. Currently, research of intelligent traffic event recognition system based on video processing is still at the exploratory stage that many key technical issues remain to be resolved. In this paper, key techniques of the process of video-based traffic event recognition are studied systematically, including moving object detection, object recognition and tracking, traffic behavior analysis and traffic event recognition.Moving object detection is the basic component of video detection and surveillance, which provides essential resource for behavior analysis and event recognition. Background model is crucial to obtain the background image for detecting moving targets effectively. Generally, background model is consisting of three parts: background initialization, background representation and background update. Among them, background initialization is the premise of background representation and update, while background representation and updating is the basis to maintain and update background image in complicated scenes for a long time. A background initialization algorithm based on the stable interval sequence searching is used in the paper. All stable non-overlapping intervals in the temporal sequence of each pixel are detected to obtain probably backgrounds first, and then background set is constructed with the pixel value variable-constrained to realize background initialization. From experiments it can be seen that the algorithm proposed could overcome "pseudo-background" generated from the slow motion of large-scale targets, as well as the accurate initial background could extracted when foreground coverage is more than 50% in the training sequence. Based on background initialization, realtime background is represented by Gaussian Mixture Model whose parameters are solved by EM algorithm. In addition, for realizing long-term background maintenance, the object-level background update algorithm is presented with the consideration of foreground motion regions information detected. This algorithm can solve the problem that suspended foreground objects become one part of the background during background update process. Foreground motion regions detected are often including moving shadow which has a significant impact on feature representation, classification and tracking of moving targets. So object-level moving shadow detection model based on RGB color variable degree is utilized to eliminate moving shadow of multi-objects effectively. Tests through several video sequences obtained from realistic scenarios show that proposed methods have good robustness and self-adaptability. This part of research enriches theories and methods of video detection and surveillance, as well as lays the foundation for traffic event recognition based on video processing.Moving objects'categories and spatio-temporal motion information could be obtained from object classification and tracking. In the aspect of the moving object recognition and classification, two basic issues are focused on: feature selection and representation, as well as classification model choice and learning. In order to provide information of target types of mixed traffic, a simple effective feature representation algorithm based on'centro-bias'moment is proposed. Centro-bias moments feature has the invariability of rotation, translation and scale, which can overcome the influence of the moving status and dynamic environment. Meantime, object velocity is extracted as the motion feature. Combination of these two representation feature, multi-class support vector machine (SVM) is used to construct optimal hyperplanes for classifying moving object into vehicles, bikes and pedestrians. Experimental results show that the classification accuracy rate can reach 89.4%. In the aspect of motion tracking of multi-class objects in mixed traffic, multi-feature matching method based on Kalman filter is developed, which is combined with motion feature, shape feature and color feature. Additionly, to solve the problem of temporary trajectory missing caused by occlusion in the process of tracking, occlusion handling method based on historical motion information compensation is proposed. The method ensures an accurate estimate of the state of motion in the complex environment. The proposed methods are validated under different traffic scenes. Results show that the proposed tracking method is robust and adaptive, and has a good real-time property that the processing speed is below 0.02 seconds / frame. This part of research provides effective technical means to obtain object feature information for behavior analysis and event recognition.Traffic objects motion pattern and abnormal behavior can be obtained by trajectory distribution learning. In order to solve limitations that most exist methods of behavior pattern recognition rely on spatial characteristics, multi-level trajectory pattern learning algorithm is presented considering spatial characteristic, orientation characteristic and type characteristic. First, improved Hausdorff distance measure approach is utilized to construct spatial similarity matrix of a collection of traces and spectral clustering is used to realizing spatial pattern learning. Secondly, distribution of trace begin-points and end-points are fitted by GMM model. Orientation pattern can be learn From this. Thirdly, trace type pattern is obtained by hierarchical clustering algorithm with object categories. Multi-level trajectory pattern learning algorithm proposed is tested with realistic scenarios videos and good performances are showed in the experimental results. Abnormal behavior detection algorithms based on spatial pattern matching and orientation pattern matching are proposed respectively. Traffic behaviors of lane-changing and reverse-driving are detected effectively through these algorithms. This part of research provides technical support to study behavior characteristics of traffic targets.Traffic event recognition not only depends on reasoning and analysis of object behavior, but also is closely related with context information. If there is a lack of context information, the meaning and content of event can't be described accurately. Hence, traffic event representation and recognition method based on context is developed and the concept of context information in traffic event is defined. Context is divided into spatial context, temporal context, object context and special parameter context according to the event content. And then, event unit is constructed with object's property and one context information. Based on this, a common semantics representation form is realized involving basic event and complex event. For traffic event recognition, basic event recognition method based on Bayes classifier combined with logical restriction and complex event recognition method based on HMM model are developed. Events of pedestrian illegal crossing and temporary parking are recognized effectively through our approaches. This part of research provides a theoretical guidance and reference for traffic event recognition system.In summary, the achievement of our research results deepens the theories and approaches of traffic event information video detection, and provides some guidance and reference meaning for the follow-up studies. On the other hand, the research has an important application value that it provides effective technical support for traffic behavioral characteristics research and intelligent traffic event information collection.