A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories

TL;DR

This paper introduces a novel tube-and-droplet representation for motion trajectories that captures global context and movement details, improving analysis tasks like clustering, classification, and action recognition.

Contribution

It proposes a new method combining thermal transfer fields, 3D tubes, and droplet vectors to effectively represent and analyze motion trajectories with enhanced contextual information.

Findings

Outperforms state-of-the-art algorithms in trajectory analysis tasks.

Effectively captures global motion patterns and context.

Improves accuracy in trajectory clustering, classification, and action recognition.

Abstract

Trajectory analysis is essential in many applications. In this paper, we address the problem of representing motion trajectories in a highly informative way, and consequently utilize it for analyzing trajectories. Our approach first leverages the complete information from given trajectories to construct a thermal transfer field which provides a context-rich way to describe the global motion pattern in a scene. Then, a 3D tube is derived which depicts an input trajectory by integrating its surrounding motion patterns contained in the thermal transfer field. The 3D tube effectively: 1) maintains the movement information of a trajectory, 2) embeds the complete contextual motion pattern around a trajectory, 3) visualizes information about a trajectory in a clear and unified way. We further introduce a droplet-based process. It derives a droplet vector from a 3D tube, so as to characterize the high-dimensional 3D tube information in a simple but effective way. Finally, we apply our tube-and-droplet representation to trajectory analysis applications including trajectory clustering, trajectory classification & abnormality detection, and 3D action recognition. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our approach.