Due to the importance of offline consumer behavior, more and more people had begun to study consumer behavior in store. In offline consumer behavior research, the application of video analysis technology was the most direct and convenient. Recognizing human posture was a key technology in video analysis. The OpenPose algorithm was one of the advantageous technologies that could accurately recognize multi-person poses in different environments in real time, so we used it innovatively to study consumer behavior in store. We hope to develop the potential of this application in the research of consumer behavior in store in the footwear retail industry by the technical advantages of the OpenPose algorithm. In our study, we first used an OpenPose algorithm to estimate multi-person pose and detection behavior, and then processed and recognized the videos collected in the store. We collected a week's surveillance video of a Red Dragonfly offline store from July 10 to July 16, 2020 in China. The specific process was to calibrate the area in the selected camera screen, then the algorithm performs identification and detection, and finally output in-store consumption Behavioral data. Our research results not only verified the feasibility of this application in offline retailing stores, but the data results also indicated that consumers tend to enter the store from the right, staying concentrated in the middle and back of the store. These results may be affected by the store space, product display, and staff guidance and reception.
Real-Time Index Authentication for Event-Oriented Surveillance Video Query using Blockchain