Person re‐identification in the edge computing system: A deep square similarity learning approach

Abstract

SummaryThe proliferation of mobile phones and webcams has led to an exponential increase in video data. One of the key technologies of video surveillance systems is Person Re‐identification (Re‐ID). The Re‐ID is used to identify whether the target pedestrian is the same person, and through scene matching, cross‐field tracking and track prediction of suspected pedestrians can be achieved. The edge computing has become the first choice for video analysis and processing, because of shorter response time and more efficient processing. In this paper, we propose a deep square similarity learning (DSSL), which considers the difference correlation, first‐order correlation, and two‐order correlation of image pairs. The training data automatically adjusts the network parameters and the weights of the three correlations to minimize the loss of the training set. Moreover, we conducted experiments on the challenging Re‐ID databases CuHK03 and Male1501. Compared with algorithm IDLA and DHSL, the first recognition rate is increased by 18% and 40%, respectively, in CuHK03, and 22% and 80% in Male1501. Then, we propose an online deep square similarity learning (ODSSL) algorithm to solve problem of data updating after the model is established by DSSL strategy. Meanwhile, ODSSL shows shorter update time and more efficient processing.