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Shopper Behavior Recognition for In-Store Merchandising using Camera Image

Shohji Ohtsubo, Takaaki Moriyama, Takeshi Ishihara, Tomoyuki Karibe, Sugiri Pranata, Yan Xu
Panasonic Technical Journal Vol. 61 No. 2 Nov 2015

We are developing a system to detect and analyze human behavior for retail stores, which extracts undiscovered behavioral features of shoppers from camera images, in order to propose innovative ideas for in-store merchandising. Human detection and tracking based on image feature matching is likely to fail in an environment with various backgrounds, such as in crowded stores. Therefore, we have implemented a method of human detection based not only on Histogram of Oriented Gradients (HOG) descriptors but also on Histogram of Depth Difference (HDD) descriptors acquired from machine learning an image from an RGB-Depth sensor. As a result, we have succeeded in reaching a precision of 99% and recall rate of 97% for human detection, even for cases which can rarely be detected only by using HOG from an RGB image. We also discovered that we could reduce the error rate of human tracking by increasing the frame rate of the image rather than the quality.

Link: https://www.panasonic.com/jp/corporate/technology-design/ptj/pdf/v6102/p0117.pdf

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