Person Monitoring by Full Body Tracking in Uniform Crowd Environment

TL;DR

This paper introduces a new dataset of full body tracking in uniform crowd environments, and demonstrates that fine-tuning a state-of-the-art tracker improves its performance in these challenging scenarios.

Contribution

The authors created a novel annotated dataset for person tracking in uniform crowds and showed that fine-tuning enhances tracker accuracy in such environments.

Findings

Fine-tuning improves tracker performance on the new dataset.

The dataset includes diverse scenarios with occlusions and view blockages.

Enhanced tracking accuracy demonstrated through quantitative metrics.

Abstract

Full body trackers are utilized for surveillance and security purposes, such as person-tracking robots. In the Middle East, uniform crowd environments are the norm which challenges state-of-the-art trackers. Despite tremendous improvements in tracker technology documented in the past literature, these trackers have not been trained using a dataset that captures these environments. In this work, we develop an annotated dataset with one specific target per video in a uniform crowd environment. The dataset was generated in four different scenarios where mainly the target was moving alongside the crowd, sometimes occluding with them, and other times the camera's view of the target is blocked by the crowd for a short period. After the annotations, it was used in evaluating and fine-tuning a state-of-the-art tracker. Our results have shown that the fine-tuned tracker performed better on the evaluation dataset based on two quantitative evaluation metrics, compared to the initial pre-trained tracker.