Adaptive Local Spatiotemporal Features from RGB-D Data for One-Shot Learning Gesture Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 12; p. 2171
• Main Authors: Lin, Jia, Ruan, Xiaogang, Yu, Naigong, Yang, Yee-Hong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.12.2016; MDPI
• Subjects: adaptive; Adaptive filters; Body parts; Corners; Filtering; Gesture recognition; Learning; Mathematical analysis; motion region of interest; Noise; Noise reduction; one-shot learning; optical flow; spatiotemporal feature; State of the art; motion region of interest; spatiotemporal feature; adaptive; one-shot learning; optical flow; gesture recognition
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16122171

Noise and constant empirical motion constraints affect the extraction of distinctive spatiotemporal features from one or a few samples per gesture class. To tackle these problems, an adaptive local spatiotemporal feature (ALSTF) using fused RGB-D data is proposed. First, motion regions of interest (MRoIs) are adaptively extracted using grayscale and depth velocity variance information to greatly reduce the impact of noise. Then, corners are used as keypoints if their depth, and velocities of grayscale and of depth meet several adaptive local constraints in each MRoI. With further filtering of noise, an accurate and sufficient number of keypoints is obtained within the desired moving body parts (MBPs). Finally, four kinds of multiple descriptors are calculated and combined in extended gradient and motion spaces to represent the appearance and motion features of gestures. The experimental results on the ChaLearn gesture, CAD-60 and MSRDailyActivity3D datasets demonstrate that the proposed feature achieves higher performance compared with published state-of-the-art approaches under the one-shot learning setting and comparable accuracy under the leave-one-out cross validation.