The capability to track individuals in CCTV cameras is important for surveillance and forensics alike. However, it is laborious to do over multiple cameras. Therefore, an automated system is desirable. In literature several methods have been proposed, but their robustness against varying viewpoints and illumination is limited. Hence performance in realistic settings is also limited. In this paper, we present a novel method for the automatic re-identification of persons in video from surveillance cameras in a realistic setting. The method is computationally efficient, robust to a wide variety of viewpoints and illumination, simple to implement and it requires no training. We compare the performance of our method to several state-of-the-art methods on a publically available dataset that contains the variety of viewpoints and illumination to allow benchmarking. The results indicate that our method shows good performance and enables a human operator to track persons five times faster.
To improve security, the number of surveillance cameras is rapidly increasing. However, the number of human operators remains limited and only a selection of the video streams are observed. Intelligent software services can help to find people quickly, evaluate their behavior and show the most relevant and deviant patterns. We present a software platform that contributes to the retrieval and observation of humans and to the analysis of their behavior. The platform consists of mono-and stereo-camera tracking, re-identification, behavioral feature computation, track analysis, behavior interpretation and visualization. This system is demonstrated in a busy shopping mall with multiple cameras and different lighting conditions.
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