Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis

• URL: http://arxiv.org/abs/2007.04942v1
• Date: Thu, 9 Jul 2020 17:14:15 GMT
• Title: Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis
• Authors: Robin Schrijvers, Steven Puttemans, Timothy Callemein and Toon Goedem\'e
• Abstract summary: Shopping behaviour analysis through counting and tracking of people in shop-like environments offers valuable information for store operators. We implement a real-time 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.
• Score: 1.0514231683620514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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