Fast trajectory extraction and pedestrian dynamics analysis using deep neural network

Ruolong Yi, Mingyu Du, Weiguo Song and Jun Zhang

Physica A: Statistical Mechanics and its Applications, 2024, vol. 638, issue C

Abstract: Pedestrian evacuation dynamics research is crucial for safety engineering and crowd management. Accurate and fast extraction of pedestrian trajectories from experimental videos is essential for reliable evacuation data and effective strategy development. In this paper, we propose a novel method for extracting pedestrian trajectories from evacuation experiment videos based on deep learning techniques. The method consists of two modules: pedestrian detection and location prediction, which use Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, respectively. Experimental results show that our method achieves high accuracy with short time compared to traditional methods. Furthermore, our experiments demonstrate that our method can accurately extract information on pedestrian evacuation dynamics. The proposed method provides a fast and reliable approach to extracting pedestrian evacuation dynamics information, which has the potential to be utilized by researchers, urban planners, and emergency management personnel to develop more effective evacuation strategies, improve crowd management, and ultimately enhance public safety.

Keywords: Pedestrian dynamics; Trajectory extraction; Deep learning; Crowd safety (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:638:y:2024:i:c:s0378437124001195

DOI: 10.1016/j.physa.2024.129611

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