Active image pair selection for continuous person re-identification

Most traditional multi-camera person re-identification systems rely on learning a static model on tediously labeled training data. Such a framework may not be suitable for situations when new data arrives continuously or all the data is not available for labeling beforehand. Inspired by the `value of information' active learning framework, we propose a continuous learning person re-identification system with a human in the loop. In brief, we term this `continuous person re-identification'. The human in the loop not only provides labels to the incoming images but also improves the learned model by providing most appropriate attribute based explanations. These attribute based explanations are used to learn attribute predictors along the way. The overall effect of such a stratgey is that starting with a few annotated images, the system begins to improve via a symbiotic relationship between the man and the machine. The machine assists the human to speed the annotation and the human assists the machine to update itself with more annotation so that more and more distinct persons are re-identified as more and more images come in. Using a benchmark dataset, we validate our approach and compare with state-of-the-art methods.