No abstract

Public security monitoring is a hot issue that the government and citizens pay close attention to. Multiobject tracking plays an important role in solving many problems for public security. Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. There are still many deficiencies in the research of multiobject trajectory prediction, which mostly employ object detection and data association. Compared with the tremendous progress in object detection, data association still relied on hand-crafted constraints such as group, motion, and spatial proximity. Emergencies usually have the characteristics of mutation, target diversification, low illumination, or resolution, which makes multitarget tracking more difficult. In this paper, we harness the advance of the deep learning framework for data association in object tracking by jointly modeling pedestrian features. The proposed deep pedestrian tracking SSD-based model can pair and link pedestrian features in any two frames. The model was trained with open dataset, and the results, accuracy, and speed of the model were compared between normal and emergency or violent environment. The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. The detection speed of the normal data set is slightly higher than that of the abnormal data set. In general, the model has good tracking results and credibility for multitarget tracking in emergency environment. The research provides technical support for safety assurance and behavior monitoring in emergency environment.

The study of evacuation for buildings with limited space is an important part of improving evacuation efficiency and preventing stampedes. A building evacuation model was proposed based on cellular automata simulation considering different crowd states. Different flow sizes under layout environments with the same facilities as well as evacuation efficiency, bottleneck area density, and escape routes choice under the orderly and disorderly distribution conditions have also been analyzed. The results show that the initial disorderly distribution state is superior to the orderly distribution state in terms of the evacuation efficiency index. The former provides evacuees with maximum room for the corridor and the exit, with the overall evacuation density being lower than that of the latter. Evacuation along the central corridor provides more room compared to that of the two flanks, which explains why evacuees prefer to occupy the central area when space is limited, and this is detrimental to the moving capacity.

No abstract

No abstract