Who is the hero? semi-supervised person re-identification in videos

Given a crowd-sourced set of videos of a crowded public event, this paper addresses the problem of detecting and re-identifying all appearances of every individual in the scene. The persons are ranked according to the frequency of their appearance and the rank of a person is considered as the measure of his/her importance. Grouping appearances of every person from such videos is a very challenging task. This is due to unavailability of prior information or training data, large changes in illumination, huge variations in camera viewpoints, severe occlusions and videos from different photographers. These problems are made tractable by exploiting a variety of visual and contextual cues i.e., appearance, sensor data and co-occurrence of people. A unified framework is proposed for efficient person matching across videos followed by their ranking. Experimental results on two challenging video data sets demonstrate the effectiveness of the proposed algorithm.