Crowd gathering events deeply affect public safety. To enhance city management and avoid potential risks, many algorithms are designed for crowd analysis and deployed on video surveillance. Widely applied deep learning models also can be trained for crowd analysis. However, there are still few works focusing on crowd gathering behavior. Furthermore, as a result of the lack of interpretability of deep learning models, which also brings potential risk of being rejected by the users. In this paper, we categorize crowd behaviors into wandering, merging, walking gathering, standing gathering, and dispersing. Also, we propose an interpretable framework for crowd gathering understanding based on crowd density estimation model and proposed crowd descriptors, named Irregularity, Sparsity, Randomness, and Volatility. The experiments on the PETS2009 dataset demonstrate our method has outperformed the previous works on the crowd gathering understanding task. Moreover, we further analyze the framework performance with different crowd feature extraction models and the relations between our descriptors and crowd behavior. Besides, an ablation study is conducted to investigate the effectiveness of the descriptors and differences between density estimation models. The results demonstrate the effectiveness and the much better interpretability of our framework. Our descriptors also show significant contributions to the quantification of crowd gathering behaviors.

This work emphasizes the current research status of the urban Digital Twins to establish an intelligent spatiotemporal framework. A Geospatial Artificial Intelligent (GeoAI) system is developed based on the Geographic Information System and Artificial Intelligence. It integrates multi-video technology and Virtual City in urban Digital Twins. Besides, an improved small object detection model is proposed: YOLOv5-Pyramid, and Siamese network video tracking models, namely MPSiam and FSSiamese, are established. Finally, an experimental platform is built to verify the georeferencing correction scheme of video images. The experimental results show that the Multiply-Accumulate value of MPSiam is 0.5B, and that of ResNet50-Siam is 4.5B. Besides, the model is compressed by 4.8 times. The inference speed has increased by 3.3 times, reaching 83 Frames Per Second. 3% of the Average Expectation Overlap is lost. Therefore, the urban Digital Twins-oriented GeoAI framework established here has excellent performance for video georeferencing and target detection problems.

This work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things (IoT) and achieve an actual intelligent fire rescue. A smart fire protection information system was designed based on the IoT. A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue. The intelligent fire visualization platform based on the three-dimensional (3D) Geographic Information Science (GIS) covers project overview, equipment status, equipment classification, equipment alarm information, alarm classification, alarm statistics, equipment account information, and other modules. The live video accessed through the visual interface can clearly identify the stage of the fire, which facilitates the arrangement of rescue equipment and personnel. The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization: emergency rescue time and the number of vehicles. In addition, an evacuation path optimization method based on the Improved Ant Colony (IAC) algorithm was designed to realize the dynamic optimization of building fire evacuation paths. The experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t = 17s than the initial value. In addition, the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation, demonstrating that this model could detect fire. The IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route. Therefore, the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.