Journal
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW 2017)
Volume -, Issue -, Pages 3047-3055Publisher
IEEE
DOI: 10.1109/ICCVW.2017.360
Keywords
-
Funding
- National Natural Science Foundations of China [61472302, U1404620, 61672409]
- Open Projects Program of National Laboratory of Pattern Recognition [201600031]
- Fundamental Research Funds for the Central Universities [JB150317]
- Natural Science Foundation of Shaanxi Province [2010JM8027]
- Aviation Science Foundation [2015ZC31005]
Ask authors/readers for more resources
Gesture recognition is an important issue in computer vision. Recognizing gestures with videos remains a challenging task due to the barriers of gesture-irrelevant factors. In this paper, we propose a multimodal gesture recognition method based on a ResC3D network. One key idea is to find a compact and effective representation of video sequences. Therefore, the video enhancement techniques, such as Retinex and median filter are applied to eliminate the illumination variation and noise in the input video, and a weighted frame unification strategy is utilized to sample key frames. Upon these representations, a ResC3D network, which leverages the advantages of both residual and C3D model, is developed to extract features, together with a canonical correlation analysis based fusion scheme for blending features. The performance of our method is evaluated in the Chalearn LAP isolated gesture recognition challenge. It reaches 67.71% accuracy and ranks the 1st place in this challenge.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available