An Effective and Efficient Method for Detecting Hands in Egocentric Videos for Rehabilitation Applications
arxiv(2019)
摘要
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
performing activities of daily living in a home simulation laboratory. Results:
The F1-scores for the best detector and tracker alone (SSD and Median Flow)
were 0.90±0.07 and 0.42±0.18, respectively. The best combination
method, in which a detector was used to initialize and reset a tracker,
resulted in an F1-score of 0.87±0.07 while being two times faster than the
fastest detector alone. Conclusion: The combination of the fastest detector and
best tracker improved the accuracy over online trackers while improving the
speed of detectors. Significance: The method proposed here, in combination with
wearable cameras, will help clinicians directly measure hand function in a
patient's daily life at home, enabling independence after SCI.
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