Abstract
With the advance of mobile technologies, mobile devices such as unmanned aerial vehicle (UAV) become more important in video surveillance. By applying mobile person re-identification (re-id), mobile devices can monitor pedestrians in the transportation system from complex environments. Since the computing and storage resources of mobile devices are limited, traditional person re-id methods are not appropriate for mobile condition. Besides, mobile person re-id task also requires real-time processing. In this paper, we propose a novel hashing method: online discrete anchor graph hashing (ODAGH) for mobile person re-id. ODAGH integrates the advantages of online learning and hashing technology. In ODAGH, we propose an online discrete optimization algorithm to improve the efficiency of anchor graph learning in the online scenario. Experimental results demonstrate the superiority of ODAGH in terms of both effect and efficiency.
Highlights
With person re-identification technology, it will be able to find the same identity from different and nonoverlapping cameras
In online discrete anchor graph hashing (ODAGH), we propose online anchor graph learning which first uses anchor graph to reduce the space cost of graph construction and uses an online learning algorithm to optimize the graph model effectively and efficiently. e main contributions of this paper are summarized as follows: (i) ODAGH integrates the advantages of online learning and hashing, and as a hashing method, it can be applied to mobile systems with limited computing and storage resources
OAGH is an unsupervised method, it can effectively preserve visual correlation of images in hashing codes. e performance of two offline hashing methods is much worse than other methods, even though they are supervised methods. e main reason is that they cannot support online learning of new images, and they only use 1000 initial images for training. e mean average precision (MAP) scores obtained by the non-hashing method KISSME are very close to OAGH. e main reason is that hashing will introduce quantization loss
Summary
With person re-identification (re-id) technology, it will be able to find the same identity from different and nonoverlapping cameras. Person re-id can be widely used for video surveillance; person re-identification is the key technology in pedestrian traffic monitoring [1]. Detecting and tracking a person across camera is important in traffic monitoring system [2]. Person re-id technology can intelligently and efficiently identify and track pedestrians in streets, airports, or other transportation systems. E task of person re-id is an image retrieval problem. Traditional person re-id technology is used in the scene where cameras are unable to move, such as fixed camera networks in different public areas, including urban transport systems
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