# Semantic segmentation of trajectories with agent models

Source: [Semantic segmentation of trajectories with agent models](<https://www.sota2.com/research/paper/68fa64151e209131cea02aaf>)

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

Data updated: 2025-10-25T20:56:11.088Z

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

Published: 2018-02-27T00:39:13.000Z

## Abstract

In many cases, such as trajectories clustering and classification, we often divide a trajectory into segments as preprocessing. In this paper, we propose a trajectory semantic segmentation method based on learned behavior models. In the proposed method, we learn some behavior models from video sequences. Next, using learned behavior models and a hidden Markov model, we segment a trajectory into semantic segments. Comparing with the Ramer-Douglas-Peucker algorithm, we show the effectiveness of the proposed method.

## 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/1802.09659>)

[PDF](<https://arxiv.org/pdf/1802.09659.pdf>)