LoCount: Long-distance Crowd Counting Based on LoRa Signal

Sihan Ma, Xiangmao Chang, Yifan Zhang, Lele Zheng

Abstract: Crowd counting, which counts or estimates the number of people within a region, is critical in many applications, such as guided tours and disaster rescue. Several RF-based contact-free crowd counting techniques have been proposed in recent years, including WiFi, RFID, and millimeter wave radar. While promising in many aspects, one key limitation of current techniques is the small sensing range. However, many applications of crowd counting do require long-range sensing capability. In this work, we propose LoCount to significantly increase the sensing range of crowd counting using LoRa, which is a new wireless technology for long range communications among IoT devices. In particular, to solve the system performance degradation caused by different environments, we try to remove the components representing surrounding environments from the signal and use adversarial domain adaptation to extract environment-independent features. Considering that we may have multiple different source domains, for the target domain data, we comprehensively think over its similarity to each source domain and the prediction results to get the final result. We test LoCount in multiple large-scale scenes, and the results show that LoCount can achieve an average accuracy of 97.0% in the target domain without labeled data.

External IDs:dblp:conf/msn/MaCZZ23