论文标题
实时嵌入式人员检测和追踪购物行为分析
Real-time Embedded Person Detection and Tracking for Shopping Behaviour Analysis
论文作者
论文摘要
购物行为通过计数和跟踪在商店式环境中的人员进行计数和跟踪为商店运营商提供了宝贵的信息，并在商店布局中提供了关键的见解（例如，经常访问的景点）。除了为此使用额外的员工，而是首选自动的本地解决方案。这些自动化系统应具有成本效益，最好是在轻巧的嵌入式硬件上，在非常具有挑战性的情况下（例如处理闭塞）工作，最好是实时工作。我们通过在Jetson TX2硬件平台上实现实时张力优化基于Yolov3的行人探测器来解决这一挑战。通过将检测器与稀疏的光流跟踪器相结合，我们为每个客户分配了一个唯一的ID，并解决了失去部分遮挡客户的问题。我们的基于检测器的解决方案以10 fps的处理速度达到了81.59％的平均精度。除了有价值的统计数据外，还提取了经常访问的斑点的热图并将其用作视频流的覆盖层。
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.
