Repairing People Trajectories Based on Point Clustering

TL;DR

This paper introduces a scene-adaptive method for repairing object trajectories in tracking systems by clustering lost and found zones, leveraging scene semantics and confidence filtering to improve accuracy without relying on predefined scene context.

Contribution

The method uniquely combines zone clustering, scene semantics, and confidence filtering to repair trajectories, avoiding dependence on predefined scene models.

Findings

Improves trajectory accuracy by repairing errors using zone triplets.

Does not require predefined scene context, increasing adaptability.

Effectively filters noisy trajectories based on confidence values.

Abstract

This paper presents a method for improving any object tracking algorithm based on machine learning. During the training phase, important trajectory features are extracted which are then used to calculate a confidence value of trajectory. The positions at which objects are usually lost and found are clustered in order to construct the set of 'lost zones' and 'found zones' in the scene. Using these zones, we construct a triplet set of zones i.e. three zones: In/Out zone (zone where an object can enter or exit the scene), 'lost zone' and 'found zone'. Thanks to these triplets, during the testing phase, we can repair the erroneous trajectories according to which triplet they are most likely to belong to. The advantage of our approach over the existing state of the art approaches is that (i) this method does not depend on a predefined contextual scene, (ii) we exploit the semantic of the scene and (iii) we have proposed a method to filter out noisy trajectories based on their confidence value.