Theory And Methodology Of Video Based Pedestrian Detection On Urban Traffic System

Transportation is the lifeline of economic development. China's transportation equipment and core technology level still has disparity from newly industrialized countries. Transportation supply ability is insufficient, different transportation modes lack coordination. The problems of energy consumption and environmental pollution seriously affect the operational efficiency of the entire society. After all, traffic problem has become the main bottleneck of economic and social development. "National Long-term Scientific and Technological Development Plan (2006-2020)" points out the challenge of transportation science and technology. It also points out the need of giving priority to the development of intelligent transportation systems (ITS).ITS is an transportation management system which integrates data communication technology, electronic sensing technology, advanced information technology, computer technology, etc. synthetically and applies them to the whole traffic management system. It has become an important way to solve traffic congestion and improve the transportation efficiency. Traffic data collection using computer vision technology is an important part of ITS. Video sensors excel many commonly used detectors due to their competitive cost, easy installation, operation and maintenance, and their ability to monitor wide areas and capture global and specific traffic data. With the development of computer vision and pattern recognition technologies, many researchers in developed countries have diving into the researches of traffic monitoring. However, most of them are concentrated on vehicles and rarely applied to non-motorized transportation modes. So they are inapplicable for mixed traffic flows which include vehicles, bicycles and pedestrians. In this condition, it is urgent to develop pedestrian detection algorithms in China, where mixed traffic flow is the major property of traffic. At present, the researches on video based traffic data collection of pedestrians and cyclists have being progressed in our country, which have demonstrated tremendous potentials of traffic application.Video based pedestrian detection methods for different traffic states are explored in this paper. It aims at extracting some important traffic parameters using traffic video sequences, which are captured by a static monocular camera in traffic scenes. Many computer vision and pattern recognition algorithms are employed. To verify the performance of the methods provided in this paper, real-world videos are tested after introduction of every pedestrian detection model respectively.What it follows contains the detailed innovations of this dissertation:1. Investigations have been carried to the video characteristics from different angles. and corresponding pedestrian flow detection methods are established respectively in this paper. To meet the need of traffic study, a traffic parameters detection method is proposed, which can capture pedestrian walking speed characteristic, pedestrian start-up time and acceleration/deceleration characteristic etc.2. A moving-based pedestrian flow detection method is provided for tilted camera configuration. This method mainly contains five modules:moving detection module, shadow remove module, feature extraction module, tracking module, and recognition module. Moving detection module aims at segmenting regions corresponding to moving objects from the rest of the image. The essential task of motion detection is to obtain an adaptive background. In order to get such background, Gaussian Mixture Model (GMM) is used for background subtraction. The main purpose of tracking module is to identify the objects correspondences between frames. To make the tracking algorithm more robust and reduce the cost of search, a prediction and matching approach is applied. In this module, the most challenging part of this system, Kalman filter (KF) is utilized to get trajectories. In classification module, in order to identify pedestrians and bicycles, Back Propagation Neural Network (BPNN) is employed. Two other simple but effective algorithms are used to alleviate the negative impacts of shadows and occlusions.3. A head-based pedestrian flow detection method is proposed for vertical camera configuration. This method mainly contains three modules:detection rectangle configuration, human head detection and matching, where human head detection is the most important part. In this module, mixed color algorithm is utilized to locate candidate human head, and then Canny algorithm is proposed to extract head contours. Finally, Hough transform is used to locate human head by head shape features.4. To capture the main traffic parameters of pedestrian, a pedestrian detection method is presented. This method mainly contains two modules:tracking and camera calibration. In camera calibration module, internal and external camera parameters are obtained by two-step approach, and then 2-D image reconstruction algorithm is utilized to transform image coordinate to world coordinate. In tracking module, optical flow algorithm is used to track the moving objects and then the data are saved automatically.