P2P-Loc: Point to Point Tiny Person Localization
Xuehui Yu, Di Wu, Qixiang Ye, Jianbin Jiao, Zhenjun Han

TL;DR
P2P-Loc introduces a point-based localization method for tiny persons that reduces annotation effort by 80%, using self-refinement to improve accuracy despite label uncertainty.
Contribution
The paper presents a novel point annotation framework and self-refinement method for efficient and accurate tiny person localization, reducing annotation costs significantly.
Findings
Achieves comparable localization performance to bounding-box methods.
Reduces annotation cost by up to 80%.
Self-refinement improves label reliability and accuracy.
Abstract
Bounding-box annotation form has been the most frequently used method for visual object localization tasks. However, bounding-box annotation relies on a large amount of precisely annotating bounding boxes, and it is expensive and laborious. It is impossible to be employed in practical scenarios and even redundant for some applications (such as tiny person localization) that the size would not matter. Therefore, we propose a novel point-based framework for the person localization task by annotating each person as a coarse point (CoarsePoint) instead of an accurate bounding box that can be any point within the object extent. Then, the network predicts the person's location as a 2D coordinate in the image. Although this greatly simplifies the data annotation pipeline, the CoarsePoint annotation inevitably decreases label reliability (label uncertainty) and causes network confusion during…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsVideo Surveillance and Tracking Methods · Human Pose and Action Recognition · Anomaly Detection Techniques and Applications
