Person Monitoring by Full Body Tracking in Uniform Crowd Environment

• URL: http://arxiv.org/abs/2209.01274v1
• Date: Fri, 2 Sep 2022 21:21:47 GMT
• Title: Person Monitoring by Full Body Tracking in Uniform Crowd Environment
• Authors: Zhibo Zhang, Omar Alremeithi, Maryam Almheiri, Marwa Albeshr, Xiaoxiong Zhang, Sajid Javed, Naoufel Werghi
• Abstract summary: In the Middle East, uniform crowd environments are the norm which challenges state-of-the-art trackers. In this work, we develop an annotated dataset with one specific target per video in a uniform crowd environment. The dataset was used in evaluating and fine-tuning a state-of-the-art tracker.
• Score: 10.71804432329509
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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