Recently, a new IoT structure known as the Artificial Intelligence of Things (AIoT) comes into play. Crowd counting is a promising field in data analysis of AIoT, however, due to poor transparency and high data security risks, developing a novel network architecture that can precisely elevate the counting of heavy crowd is extremely difficult. In addition, the fusion of IoT and AI also poses several challenges. The focus of this work is on the effective design of IoT framework and deep learning algorithm towards security of smart city. The system can be used to estimate the crowd traffic in public places, and can prevent the occurrence of congestion, stampede and other accidents, such as stations, airports, large-scale exhibitions, tourist attractions and other places. The constructed system contains video collection, upload and display as well as data analysis and early warning operation at the embedded device end, and automatically tracks densely crowd areas by controlling the video monitoring device. Moreover, the cloud platform can be controlled through the network. Our proposed algorithms are composed of two main aspects, i.e., division and focus. Firstly, we propose a novel density-adaptive Gaussian kernel to elevate the quality of density maps. Then, we propose a module based on conditional random fields for feature fusion. Finally, we propose a block segmentation module to predict our segmentation results and extract the context-aware information in segmentation stage. Experiments on our captured data, the Shanghai Tech, UCF_CC_50 and UCF_QNRF datasets demonstrate that our solution has obtained better performance and lower count errors over the state of the art.
All Science Journal Classification (ASJC) codes