Towards Deep Learning based Hand Keypoints Detection for Rapid Sequential Movements from RGB Images
This work addresses the problem of cognitive assessment through hand gesture analysis for researchers and practitioners in computer vision, but it is incremental as it focuses on benchmarking rather than new methods.
The paper tackles hand keypoint detection for rapid finger movements by introducing a novel benchmark dataset for cognitive behavior monitoring and evaluating state-of-the-art methods on it, providing quantitative performance results.
Hand keypoints detection and pose estimation has numerous applications in computer vision, but it is still an unsolved problem in many aspects. An application of hand keypoints detection is in performing cognitive assessments of a subject by observing the performance of that subject in physical tasks involving rapid finger motion. As a part of this work, we introduce a novel hand key-points benchmark dataset that consists of hand gestures recorded specifically for cognitive behavior monitoring. We explore the state of the art methods in hand keypoint detection and we provide quantitative evaluations for the performance of these methods on our dataset. In future, these results and our dataset can serve as a useful benchmark for hand keypoint recognition for rapid finger movements.