- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHU Jun, ZHANG Tianyi, XIE Yakun, ZHANG Jie, LI Chuangnong, ZHAO Li, LI Weilian. Intelligent Statistic Method for Video Pedestrian Flow Considering Small Object Features[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 705-712, 736. doi: 10.3969/j.issn.0258-2724.20200425
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Pedestrian flow statistics have an important value of research in the fields like intelligent security. In view of the low accuracy in pedestrian flow statistics of video surveillance systems, an intelligent statistic method of video pedestrian flow is proposed with small object features considered. Key technologies are focused in this work such as the improved Faster R-CNN (Faster region-convolutional neural network) algorithm for small object detection, moving object association and matching, and intelligent statistics of bidirectional pedestrian flow. More efforts include adapting the Faster R-CNN network structure according to the small-scale characteristics of the head object, improving the feature extraction ability of the network for small objects by using shallow features of images, and realizing real-time tracking of moving objects through the tracking algorithm based on trajectory prediction. Meanwhile, an intelligent statistic algorithm for bidirectional pedestrian flow is developed to achieve accurate statistics of pedestrian flow. To prove the effectiveness of the proposed method, the experiments were conducted in scenes with different levels of density. The results show that compared with the original algorithm, the improved object detection algorithm improves the mean average precision by 7.31% and 10.71% on the Brainwash test set and Pets2009 benchmark data set, respectively. For the intelligent statistic method of video pedestrian flow, the comprehensive evaluation index
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