Egocentric Hand Track and Object-Based Human Action Recognition

By Georgios Kapidis, Ronald Poppe, E. V. Dam, Lucas P. J. J. Noldus, Remco C. Veltkamp. 2019

40 citations recorded by the source corpus.

Egocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective. In this paper we address the challenge of egocentric human action recognition by utilizing the presence and position of detected regions of interest in the scene explicitly, without further use of visual features. Initially, we recognize that human hands are essential in the execution of actions and focus on obtaining their movements as the principal cues that define actions. We employ object detection and region tracking techniques to locate hands and capture their movements. Prior knowledge about egocentric views facilitates hand identification between left and right. With regard to detection and tracking, we contribute a pipeline that successfully operates on unseen egocentric videos to find the camera wearer's hands and associate them through time. Moreover, we emphasize on the value of scene information for action recognition. We acknowledge that the presence of objects is significant for the execution of actions by humans and in general for the description of a scene. To acquire this information, we utilize object detection for specific classes that are relevant to the actions we want to recognize. Our experiments are targeted on videos of kitchen activities from the Epic-Kitchens dataset. We model action recognition as a sequence learning problem of the detected spatial positions in the frames. Our results show that explicit hand and object detections with no other visual information can be relied upon to classify hand-related human actions. Testing against methods fully dependent on visual features, signals that for actions where hand motions are conceptually important, a region-of-interest-based description of a video contains equally expressive information with comparable classification performance.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

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Citations in the corpus

Wanderlust: Online Continual Object Detection in the Real World (2021, background): “Another related research direction is ego-centric video recognition [38, 12, 20].”
Is First Person Vision Challenging for Object Tracking? (2020, unknown): “action recognition [41], as well as forecast human-object interactions through the analysis of hand trajectories [51].”
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models (2025, unknown): “We train our base model [61] on both the Open X-Embodiment dataset [43] and action-less video datasets [17, 24], and then fine-tune the model on task demonstrations collected on downstream robot setups used for deployment and evaluation.”
Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos (2021, background): “These include attention mechanisms from gaze and motor attention [47, 38, 42], active object detection [20, 7, 15, 70], and hands in contact [66, 60, 33].”