Cascade Method for Image Processing Based People Detection and Counting
2016
People detection is of great importance in video surveillance. Different approaches have been proposed to achieve accurate detection system. The main problem in people detection systems is that it must maintain a balance between the number of false detections and the number of missing people which limits the global detection results. In order to solve this problem and add robustness to detection, we propose a multiplexor and collector model composed of multiple independent detectors. This model is used to keep the true positive detections provided by a number of detectors and reduce the miss rate. In addition, a fusion model is proposed to check the robustness of the cascaded detection system. A pipeline techniques will also be used to avoid the increasing of detection time.