Statistical Calculation of Dense Crowd Flow Antiobscuring Method considering Video Continuity

People flow statistics have important research value in areas such as intelligent security. Accurately identifying the occluded target in video surveillance is a difficulty in the video surveillance system. Now the popular moving object tracking algorithm is based on detection and cannot accurately determine the relationship between overlapping. For the statistics of people flow in the video surveillance system, a dense crowd flow antiocclusion statistical algorithm considering video continuity is proposed. This study focuses on the improved faster R-CN algorithm for small target detection, moving target correlation matching, and two-way human flow intelligent statistics. According to the small-scale characteristics of the human head target, the faster R-CNNV network structure is adaptively improved. The shallow images features are used to improve the feature extraction ability of the network for small targets. The occlusion relationship function is constructed to clearly express the relationship between the occlusion targets, and it is incorporated into the framework of the tracking algorithm. A tracking algorithm based on trajectory prediction is used to follow moving targets in real time, and a two-way human flow intelligent statistical method is used to accomplish human flow. To prove the strength of the method, tests are managed in scenes with different degrees of density, and the results show that the improved target detection algorithm improves the average accuracy of 7.31% and 10.71% on the Brainwash test set and Pets2009 benchmark data set, respectively, compared with the original algorithm. The F-value of the comprehensive evaluation index of video stream of people intelligent statistical method in various scenes can reach more than 90%. Compared with the excellent methods SSD sorting algorithm and yolov3 deepsort algorithm in recent years, its F value is increased by 1.14%–3.04%.