Generalizable person re-identification for practical automated video surveillance systems
The person re-identification (Re-ID) task aims to associate images of the same person across multiple non-overlapping camera networks. Re-ID models typically extract whole body features from images of people and match the features via proper distance metrics. Because of this approach, Re-ID-based video surveillance systems can track people without personal information such as faces, and hence effective for the systems. Following the success of deep convolutional neural networks (CNNs) in many computer vision tasks, CNNs have become de-facto standard for Re-ID models. CNNs can extract important clues to identify people from images, and as a result, achieve more than 95% of the identification accuracy. [1]
