| ABSTRACT:Currently, most monitoring of traffic scene is mainly achieved through manual supervisory control or artificial observation of video after the event. These methods need a lot of manpower, material resources; furthermore, omissions may occur abnormal situations can't be responded due to the limitation of human's energy and attention. Thus there is need to establish intelligent traffic management system that is without human intervention or requires very little manual operation. In the system, installing cameras are fixed to capture video to achieve targets location, identification and tracking. Behavior of goals (such as pedestrians, vehicles, etc.) are analyzed and judged based on the traffic parameters that are analyzed and extracted.The methods of vehicle detection, tracking and recognition have been proposed and achieved some fruits and contributions, however, pedestrian detection, tracking and recognition is relatively lagging behind. Besides, the specific situation of pedestrian is much more complex. The pedestrian area in the video is small; the action of pedestrian is random; the speed of pedestrian is not consistent; and the spacing between the pedestrians may be very small. The above problems pose a great threat on technological realization. Therefore, it is required to search and solve the difficulties of parameters extracting, including the amount of pedestrian on intersection of traffic, speed, direction and other parameters important traffic data which are important to the simulation system that can simulate the actual state of reality.The main contents of this paper include:(1) The lower quality factors of the intelligent traffic surveillance video were analyzed and various noise models are summarizes. The most common noise in intelligent traffic video, salt & pepper noise and Gaussian noise, were reduced by classical filtering algorithms. An improved algorithm was proposed in which the image pixels will be pre-judged as noise or non-noise. Only possible pixels in the region of noise were filtered while non-noise pixels were not filtered, thus the image details were remained better.(2) For pedestrian detection and identification, specific solutions and specific steps were developed to solve the difficulties. The commonly algorithms used for target detection were compared and suitable pedestrian detection algorithm was selected according to the characteristics intelligent traffic intersection surveillance video. The detected region of pedestrians is often incomplete and has empty regions, morphological operations were employed on the pedestrian region to connect the fracture of the target area and fill the holes in the target region. Small noise patches that may exist in region of pedestrians were removed by area calculation. The shadow of pedestrian was removed by geometric-based method. Various moving object models in the complex environment of intersection were established and the characteristics of pedestrian goals were summarized to achieve video-based pedestrian detection and recognition.(3) Pedestrian tracking algorithm in intelligent traffic video intersections was realized. Through calibration of the camera field, the camera space of digital image coordinates in video was mapped to the actual physical world space coordinate. A lot of important traffic parameters were extracted, including center of pedestrian target, displacement, walking speed, walking acceleration, flow rate of pedestrian traffic and so on. The valuable traffic information and data are important for the simulation system to provide realistic simulation of the actual situation.In conclusion, the results and conclusions of this research project can provide practical simulation system with the real traffic situation which analyzes the traffic situation in the junction of the transportation system. Besides, it provides the basis for comprehensive management and maintenance. Thus, this paper is not only of great theoretical significance, but also has extensive application value. |
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