Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Abstract

Objective. 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.