FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human Interaction Analysis
Zihui Guo, Yonghong Hou, Pichao Wang, Zhimin Gao, Mingliang Xu, and, Wanqing Li

TL;DR
This paper introduces FT-HID, a large-scale dataset with synchronized first and third person human interaction videos, enabling advanced joint learning methods for human motion analysis.
Contribution
The paper provides a novel large-scale, multi-modal dataset covering both FPV and TPV, and proposes new multi-view interaction and joint learning frameworks.
Findings
Validated dataset with over 90K samples from 109 subjects
Proposed multi-view interaction mechanism for skeleton sequences
Achieved promising results with new joint learning methods
Abstract
Analysis of human interaction is one important research topic of human motion analysis. It has been studied either using first person vision (FPV) or third person vision (TPV). However, the joint learning of both types of vision has so far attracted little attention. One of the reasons is the lack of suitable datasets that cover both FPV and TPV. In addition, existing benchmark datasets of either FPV or TPV have several limitations, including the limited number of samples, participant subjects, interaction categories, and modalities. In this work, we contribute a large-scale human interaction dataset, namely, FT-HID dataset. FT-HID contains pair-aligned samples of first person and third person visions. The dataset was collected from 109 distinct subjects and has more than 90K samples for three modalities. The dataset has been validated by using several existing action recognition…
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Taxonomy
TopicsHuman Pose and Action Recognition · Anomaly Detection Techniques and Applications · Gait Recognition and Analysis
