TL;DR
This paper presents a new, fast, and accurate method for detecting hands in egocentric videos, aiding at-home monitoring of hand function in SCI patients for rehabilitation.
Contribution
It introduces a simple combination of existing detection and tracking algorithms that improves accuracy and speed for hand detection in egocentric videos.
Findings
Best detector and tracker alone achieved F1-scores of 0.90 and 0.42.
Combining detector and tracker yielded an F1-score of 0.87.
The combined method is twice as fast as the best detector alone.
Abstract
Objective: Individuals with spinal cord injury (SCI) report upper limb function as their top recovery priority. To accurately represent the true impact of new interventions on patient function and independence, evaluation should occur in a natural setting. Wearable cameras can be used to monitor hand function at home, using computer vision to automatically analyze the resulting videos (egocentric video). A key step in this process, hand detection, is difficult to do robustly and reliably, hindering deployment of a complete monitoring system in the home and community. We propose an accurate and efficient hand detection method that uses a simple combination of existing detection and tracking algorithms. Methods: Detection, tracking, and combination methods were evaluated on a new hand detection dataset, consisting of 167,622 frames of egocentric videos collected on 17 individuals with SCI…
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Taxonomy
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
