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

Source: [Semantic segmentation of trajectories with improved agent models for pedestrian behavior analysis](<https://www.sota2.com/research/paper/68fa66ca1e209131cea12452>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:00:46.769Z

Authors: Toru Tamaki, Daisuke Ogawa, Bisser Raytchev, Kazufumi Kaneda

Published: 2019-12-12T02:04:43.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1912.05727>)

[PDF](<https://arxiv.org/pdf/1912.05727.pdf>)