Study of Pedestrian and Unusual Activities Detection Methods Based on Video Sequence
|School||Shanghai Jiaotong University|
|Course||Communication and Information System|
|Keywords||Pedestrian Detection Abnormal behavior detection Gradient direction histogram AdaBoost algorithm MILBoost algorithm Lucas-Kanade optical flow method|
Pedestrian Detection with abnormal behavior detection in daily intelligent transportation, intelligent monitoring , multimedia search field has a very wide range of applications . Based on the technology , we found that the occurrence of abnormal behavior the pedestrians and monitoring scenes video . However, due to the diversity of monitoring changes in the scene and the pedestrian posture , pedestrian detection and abnormal behavior detection have brought a great challenge . For more than two technologies proposed an effective detection method : (1) the method with MILBoost pedestrian detection methods based on the gradient direction histogram gradient orientation histogram AdaBoost algorithm , MILBoost - algorithm , the image in pedestrian the quick retrieval . The gradient direction histogram method to extract the characteristics of the object of detection , first characterized in the initial screening by AdaBoost , and then use the combined training MILBoost characterized screened final discriminant desired classification . By a large number of experiments comparing we can see : Add MILBoost pedestrian detection algorithm for the multi- gesture , multi-scale pedestrian good detection results . (2) The method based on optical flow monitor and connectivity described temporal behavior anomaly detection method based on the optical flow method , the monitoring of the scene is divided into a plurality of regions , each region has a monitor may be the motion of the optical flow of the abnormal alarm, according to the occurrence of abnormal behavior is a continuous process , there must be a certain amount of time and space connectivity features , in order to determine whether the occurrence of abnormal behavior . So that we effectively reduce the false detection due to the individual the noise disturbance of optical flow , and improve the efficiency of detection . This paper will use the the AdaBoost algorithm , MILBoost algorithm , gradient direction histogram features and Lucas-Kanade optical flow method and other methods . Each chapter be methods used were given a basic introduction , and then given in detail the proposed pedestrian detection method to detect the abnormal behavior .