Proceedings ArticleDOI
A Human-Object Interaction Detection Method Inspired by Human Body Part Information
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TLDR
An HOI detection method that integrates human body part information (HBP) is proposed that makes the best of the relationship between parts and objects that effectively improves detection performance.Abstract:
In recent years, with the rapid development of big data and computer hardware, deep learning has regained vitality, and the field of computer vision has made great progress. Human Object Interaction (HOI) Detection is an important subject for image understanding in computer vision, which has not been effectively solved yet. Aiming at the problem that the human pose information is not effectively used in the current HOI detection methods, an HOI detection method that integrates human body part information (HBP) is proposed. By applying human body part information to HOI detection, it makes the best of the relationship between parts and objects that effectively improves detection performance. The experiments and results on Verbs in COCO (V-COCO) dataset show the effectiveness of our method.read more
Citations
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Book ChapterDOI
Human-Object Interaction Detection Based on Multi-scale Attention Fusion.
Qianling Wu,Yongzhao Zhan +1 more
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale attention fusion method to adapt to people and objects of different sizes and shapes, which increases the range of attention which can more accurately judge the relationships between people and object.
Proceedings ArticleDOI
Performance Evaluation Between Tiny Yolov3 and MobileNet SSDv1 for Object Detection
Jahib Nawfal,Avinash Mungur +1 more
TL;DR: In this article , an evaluation framework has been proposed to assess the performance of Tiny Yolov3 and MobileNet SSD v1 for detecting people in a classroom scenario, where people were detected and counted.
Proceedings ArticleDOI
Performance Evaluation Between Tiny Yolov3 and MobileNet SSDv1 for Object Detection
TL;DR: In this paper , an evaluation framework has been proposed to assess the performance of Tiny Yolov3 and MobileNet SSD v1 for detecting people in a classroom scenario, where people were detected and counted.
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