HMM-Based Person Re-identification in Large-Scale Open Scenario

This paper aims to tackle person re-identification (person re-ID) in large-scale open scenario, which differs from the conventional person re-ID tasks but is significant for some real suspect investigation cases. In the large-scale open scenario, the image background and person appearance may change immensely. There are a large number of irrelevant pedestrians appearing in the urban surveillance systems, some of which may have very similar appearance with the target person. Existing methods utilize only surveillance video information, which can not solve the problem well due to above challenges. In this paper, we explore that pedestrians’ paths from multiple spaces (such as surveillance space and geospatial space) are matched due to temporal-spatial consistency. Moreover, people have their unique behavior path due to the differences of individual behavioral. Inspired by these two observations, we propose to use the association relationship of paths from surveillance space and geospatial space to solve the person re-ID in large-scale open scenario. A Hidden Markov Model based Path Association(HMM-PA) framework is presented to jointly analyze image path and geospatial path. In addition, according to our research scenario, we manually annotate path description on two large-scale public re-ID datasets, termed as Duke-PDD and Market-PDD. Comprehensive experiments on these two datasets show proposed HMM-PA outperforms the state-of-art methods.