Estimation of Human Orientation using Coaxial RGB-Depth Images
نویسندگان
چکیده
Estimation of human orientation contributes to improving the accuracy of human behavior recognition. However, estimation of human orientation is a challenging task because of the variable appearance of the human body. The wide variety of poses, sizes and clothes combined with a complicated background degrades the estimation accuracy. Therefore, we propose a method for estimating human orientation using coaxial RGBDepth images. This paper proposes Depth Weighted Histogram of Oriented Gradients (DWHOG) feature calculated from RGB and depth images. By using a depth image, the outline of a human body and the texture of a background can be easily distinguished. In the proposed method, a region having a large depth gradient is given a large weight. Therefore, features at the outline of the human body are enhanced, allowing robust estimation even with complex backgrounds. In order to combine RGB and depth images, we utilize a newly available single-chip RGB-ToF camera, which can capture both RGB and depth images taken along the same optical axis. We experimentally confirmed that the proposed method can estimate human orientation robustly to complex backgrounds, compared to a method using conventional HOG features.
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