Abstract
We present a method to recognize gestures made by Chinese traffic police based on the static and dynamic descriptor fusion for driver assistance systems and intelligent vehicles. Gesture recognition is made possible by combining the extracted static and dynamic features. First, the point cloud data of human upper body in each frame of input video is obtained to estimate the static descriptor with 2.5D gesture model. Then, the dynamic descriptor is estimated by computing the motion history image of the input RGB video sequence. Finally, the above two descriptors are fused and the mean structural similarity index is used to recognize the gestures made by Chinese traffic police. A comparative study and qualitative evaluation are proposed with other gesture recognition methods, which demonstrate that better recognition results can be obtained using the proposed method on a number of video sequences.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (No. 61502537, 91220301), China Postdoctoral Science Foundation (No. 2014 M552154), Hunan Planned Projects for Key Scientific Research Funds (No. 2015WK3006), Postdoctoral Science Foundation of Central South University (No. 126648).
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Guo, F., Tang, J. & Wang, X. Gesture recognition of traffic police based on static and dynamic descriptor fusion. Multimed Tools Appl 76, 8915–8936 (2017). https://doi.org/10.1007/s11042-016-3497-9
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DOI: https://doi.org/10.1007/s11042-016-3497-9