Estimating Median Visiting Times using Re-identification

Abstract: Using customer visiting times stores can analyse customer behaviour and gain insights to help improve the store experience. This thesis investigates the possibility of using person re-identification to create a system that can estimate the median visiting time. Neural networks were used to analyse images of persons from two different views. Images depicting the same person were matched together and from these matches, time differences were measured. Two camera setups were compared, one where the camera is looking vertically down, referred to as the top view. The other one with a 45 degree angle, referred to as the standard view. As part of the thesis, a benchmark dataset was created containing data from both camera setups. Different strategies to perform the image matching are presented and analysed. The presented system estimates the median visiting time with a relative error of 0.00073. The error is low enough to reliably estimate the median visiting time in a small store. For the top view camera setup we achieved a re-identification rank-1 accuracy of 84.7 % and a mAP score of 81.5 %. Comparing the two different camera installation setups leads to a couple of important lessons. The amount of standard view data publicly available is huge compared to top view giving the standard camera setup a head start. The top view system has other advantages such as being able to avoid occlusions. Ideally, the system would embrace advantages of both setups and a compromise between the two views is suggested as a future improvement.