A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment

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.

An Effective Self-Attention-Based Hybrid Model for Short-Term Traffic Flow Prediction

“…Te third category is the deep learning model, including RNN (GRU, LSTM) and CNN models. Deep learning models have shown promising performance in many research areas due to their abilities on complex model nonlinear relationships [29]. Diferent types of models for the short-term trafc prediction have been developed based on their abilities to capture the spatiotemporal correlations of trafc fow.…”

A Route Choice Model for Road Network Users in Mountainous Cities considering Vulnerability

“…Based on the above researches, a route choice model based on many reliability indexes is proposed, which studies the factors that will lead to the random changes of traffic demand and section capacity and considers the user equilibrium allocation based on reliability. e presuppositions include random change of demand or capacity [13], random change of supply and demand [15], or prediction technology based on deep learning algorithm [16][17][18][19][20][21]. Usually, considering the reliability, the route choice of travelers is often considered from two aspects [22].…”