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It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET, which can better initialize the size and position of the head detection frame in the image with the help of density map and RGBD-adaptive perception network. Results. The results show that our method achieves the best performance in RGBD dense video crowd counting on five labeled sequence datasets; the MICC dataset, CrowdFlow dataset, FDST dataset, Mall dataset, and UCSD dataset were evaluated to verify its effectiveness. Conclusion. The experimental results show that the proposed DRD-NET model combined with DRC-ConvLSTM outperforms the existing video crowd counting ConvLSTM model, and the effectiveness of the parameters of each part of the model is further proved by ablation experiments.<\/jats:p>","DOI":"10.1155\/2022\/7247757","type":"journal-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T20:05:19Z","timestamp":1660334719000},"page":"1-19","source":"Crossref","is-referenced-by-count":0,"title":["Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6186-4756","authenticated-orcid":true,"given":"Guoyin","family":"Ren","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9361-1932","authenticated-orcid":true,"given":"Xiaoqi","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010, China"},{"name":"Inner Mongolia University of Technology, Hohhot 010051, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Inner Mongolia University of Science & Technology, Baotou 014010, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","first-page":"2125","article-title":"Crowd disaster risk identification in large sport venues","volume-title":"Applied mechanics and materials. 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