Queue Waiting Time Estimation Using Person Re-identification by Upper Body
In this paper, we propose a new approach to estimating waiting time in queue based on object tracking and person re-identification by upper body. The task we are considering is practically important in video analysis, because this data can be used for predictive analytics and improvement of customer services. The main idea of the proposed method is to use upper body detections instead of full body detections. This decision is due to the following fact: in queues, the upper bodies are more visible. Using re-identification allows us to perform video analytics on sparse frames and thereby increase the computational efficiency of the estimation algorithm. Also in this work, we introduce a novel upper body regression by head, upper body random size augmentation to improve re-identification performance in real-world scenarios and a method for calculating metrics for queue waiting time estimation algorithms. Our experimental evaluation showed that the proposed algorithm has a high accuracy of queue waiting time estimation.