Semantic Person Retrieval in Surveillance Using Soft Biometrics: AVSS 2018 Challenge II
Michael Halstead, Simon Denman, Clinton Fookes, Yingli Tian, Mark S. Nixon · 2018
In surveillance and security today it is a common goal to locate a subject of interest purely from a semantic description; think of an offender description form handed into a law enforcement agency. To date, these tasks are primarily undertaken by operators on the ground either by manually searching a premises or by combing through hours of video footage. Using computer vision to attempt to partially or fully automate these tasks has been gathering interest within the research community in recent years, however, to date there has been little coordinated effort to advance the field. This has motivated the challenge that is presented in this paper: the AVSS Challenge on Semantic Person Retrieval in Surveillance Using Soft Biometrics. This challenge consists of two related tasks: person re-identification from a semantic query and person search within a video from a query. In this paper, we present the publicly available data for this challenge, the evaluation framework, and the challenge results. It is our hope that the outcomes of this challenge and the availability of the data used in this challenge will expedite research and development in this societal field.