Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis
This provides an automated, cost-effective solution for store operators to analyze shopping behavior, though it is incremental as it adapts existing methods to a specific domain.
The paper tackled real-time person detection and tracking in retail environments by implementing a YOLOv3-based detector with optical flow tracking on embedded hardware, achieving 81.59% average precision at 10 FPS and generating heat maps for behavior analysis.
Shopping behaviour analysis through counting and tracking of people in shop-like environments offers valuable information for store operators and provides key insights in the stores layout (e.g. frequently visited spots). Instead of using extra staff for this, automated on-premise solutions are preferred. These automated systems should be cost-effective, preferably on lightweight embedded hardware, work in very challenging situations (e.g. handling occlusions) and preferably work real-time. We solve this challenge by implementing a real-time TensorRT optimized YOLOv3-based pedestrian detector, on a Jetson TX2 hardware platform. By combining the detector with a sparse optical flow tracker we assign a unique ID to each customer and tackle the problem of loosing partially occluded customers. Our detector-tracker based solution achieves an average precision of 81.59% at a processing speed of 10 FPS. Besides valuable statistics, heat maps of frequently visited spots are extracted and used as an overlay on the video stream.