BROWSE
Detail
Conference Paper
H3Net: Irregular Posture Detection by Understanding Human Character and Core Structures
Cited 3 time in 
Share 
Share
- Authors
- Seungha Noh, Kangmin Bae, Yuseok Bae, Byong-Dai Lee
- Issue Date
- 2024-06
- Citation
- Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2024, pp.5631-5641
- Language
- English
- Type
- Conference Paper
- Abstract
- This paper proposes H 3 Net that considers detecting people in irregular postures by utilizing human structures and characters. To handle both features, we introduce two attention modules: 1) Human Structure Attention Module (HSAM), which is introduced to focus on the spatial aspects of a person, and 2) Human Character Attention Module (HCAM), which is designed to address the issue of repetitive appearance. HSAM effectively handles both foreground and background information about a human instance and utilizes keypoints to provide additional guidance to predict irregular postures. Meanwhile, HCAM employs ID information obtained from the tracking head, enriching the posture prediction with high-level semantic information. Furthermore, gathering images of people in irregular postures is a challenging task. Therefore, many conventional datasets consist of images with the same actors simulating varying postures in distinct images. To address this problem, we propose a Human ID Dependent Posture (HID 2 ) loss that handles repeated instances. The HID 2 loss generates a regularization term by considering duplicated instances to reduce bias. Our experiments demonstrate the effectiveness of H 3 Net compared to existing algorithms on irregular posture datasets. Furthermore, we show the qualitative results using color-coded masks and bounding boxes. We also provide ablation studies to highlight the significance of our proposed methods.