Current
Person Re-identification
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Person re-identification is a task of recognising pedestrian appearance in different time and locations captured with a multi-camera network without field of view overlap.
Due to the enormous challenges including vast variations in i) pedestrian viewpoint and pose, ii) environment illumination and occlusion, and iii) camera position, configuration and resolution, person re-identification still remains an open problem.
We proposed a Riemannian manifold based discriminative learning approach for the multiple-shot re-identification problem and achieved state-of-the art performance on five public benchmark datasets. [More] |
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Privacy protection in surveillance video data has received great attention. Although tremendous works have been proposed to provide effective privacy protection techniques, most of the algorithms are based on post-processing that deletes, obscures or encrypts the privacy information after privacy-included raw data are recorded. Consequently, they are vulnerable to raw data leak out, which may lead to unauthorized use. Therefore, it is imperative to develop a new privacy protection scheme which is capable of excluding any privacy information at the video recording phase.
We proposed an anonymous camera aiming to protect the privacy of individuals at the video capturing phase by optical masking technique. It effectively reduces the risk of raw data leakage because no privacy information will be recorded by the camera. We implemented a prototype camera, which consists of an infrared (IR) camera, a RGB camera and a liquid crystal on silicon (LCoS) device. We introduce optical design and performance of the anonymous camera, the tracking and masking algorithm as well as the calibration methodology. [More] |
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