Study Of Hybrid Methods Based Pedestrian Counting Algorithm

In real life, the technique of pedestrian counting can extract the pedestrian trafficinformation from video scenes and monitor the pedestrian mobile trends. These dataare the important references for a variety of social management services. Based oncomputer vision, the pedestrian counting process can be automatically achievedwithout spending a lot of human and material resources. Due to its efficientprocessing capabilities, the pedestrian counting algorithm has become the focus of thecurrent field of computer vision research topics.In this paper, on the basis of the research of pedestrian counting methods, wepropose a pedestrian counting approach which is integrated pre-processing moduleand counting module. In the pre-processing module, we introduce a linearinterpolation method based on key regional calibration pedestrian, which can extractthe priori information of video scenes. Meanwhile, we add some pre-processing andpost-processing operations to the background models of different levels. These stepscan provide efficient and accurate time constraints for our method. In the countingmodule, we apply bottom-up and top-down process, an imitation of the model ofhuman visual recognition, to pedestrian detection and tracking. We follow thealgorithm framework of assumption and authentication to build a model of visualattention. In the assumption stage, we introduce a fast optical flow estimation basedlocal nearest neighbor matching algorithm to form SURF feature trajectories. Thesetrajectories imply the potential pedestrians in the scene. In the authentication stage,we first design two validation rules based on the geometric characteristics and modecharacteristics of the head-shoulder part of pedestrian. And then we use Bayesianframework to achieve a clustering process of the SURF feature trajectories so as to get the locations of alternative pedestrians. At last, we verify them by the use of thetracking process integrating structural features and apparent characteristics. Afterthese two stages, we can count the number of pedestrians according to their movingtrajectories. The experimental results show that our proposed method is better than thetwo currently popular counting methods in both accuracy and efficiency.