Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis

TL;DR

This paper introduces a novel method for semantic segmentation of pedestrian trajectories using improved agent models, enhancing the understanding of pedestrian behavior from image sequence data.

Contribution

The paper advances trajectory modeling by refining the MDA approach and integrating it with hidden Markov models for better semantic segmentation.

Findings

Outperforms classical Ramer-Douglas-Peucker algorithm

Improved MDA model yields more accurate segmentation

Effective in real-world pedestrian trajectory analysis

Abstract

In this paper, we propose a method for semantic segmentation of pedestrian trajectories based on pedestrian behavior models, or agents. The agents model the dynamics of pedestrian movements in two-dimensional space using a linear dynamics model and common start and goal locations of trajectories. First, agent models are estimated from the trajectories obtained from image sequences. Our method is built on top of the Mixture model of Dynamic pedestrian Agents (MDA); however, the MDA's trajectory modeling and estimation are improved. Then, the trajectories are divided into semantically meaningful segments. The subsegments of a trajectory are modeled by applying a hidden Markov model using the estimated agent models. Experimental results with a real trajectory dataset show the effectiveness of the proposed method as compared to the well-known classical Ramer-Douglas-Peucker algorithm and also to the original MDA model.