Real-Time Moving Flock Detection in Pedestrian Trajectories Using Sequential Deep Learning Models

TL;DR

This paper presents a real-time deep learning framework for detecting pedestrian flocks and collective motion patterns, enhancing crowd analysis and autonomous navigation systems.

Contribution

It introduces a novel two-stage deep learning approach combining pairwise classification and multi-agent flock detection, validated on real-world datasets.

Findings

High accuracy in real-time flock detection

Robustness across diverse movement patterns

Extension to other collective motions

Abstract

Understanding collective pedestrian movement is crucial for applications in crowd management, autonomous navigation, and human-robot interaction. This paper investigates the use of sequential deep learning models, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Transformers, for real-time flock detection in multi-pedestrian trajectories. Our proposed approach consists of a two-stage process: first, a pre-trained binary classification model is used for pairwise trajectory classification, and second, the learned representations are applied to identify multi-agent flocks dynamically. We validate our method using real-world group movement datasets, demonstrating its robustness across varying sequence lengths and diverse movement patterns. Experimental results indicate that our model consistently detects pedestrian flocks with high accuracy and stability, even in dynamic and noisy environments. Furthermore, we extend our approach to identify other forms of collective motion, such as convoys and swarms, paving the way for more comprehensive multi-agent behavior analysis.