Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations

Soft biometrics provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals. Since the gaze represents one of the most investigated human traits, works evaluating the feasibility of considering it as a possible additional soft biometric trait have been recently appeared in the literature. Unfortunately, there is a lack of systematic studies on clinically approved stimuli to provide evidence of the correlation between exploratory paths and individual identities in “natural” scenarios (without calibration, imposed constraints, wearable tools). To overcome these drawbacks, this paper analyzes gaze patterns by using a computer vision based pipeline in order to prove the correlation between visual exploration and user identity. This correlation is robustly computed in a free exploration scenario, not biased by wearable devices nor constrained to a prior personalized calibration. Provided stimuli have been designed by clinical experts and then they allow better analysis of human exploration behaviors. In addition, the paper introduces a novel public dataset that provides, for the first time, images framing the faces of the involved subjects instead of only their gaze tracks

UA-DETRAC 2018: Report of AVSS2018 & IWT4S Challenge on Advanced Traffic Monitoring

A desirable smart traffic-monitoring and street-safety system can elicit and support the intervention of law enforcement agencies or medical staff. Recently, there has been a dramatically higher demand for such smart systems. To this end, the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S) was organized in conjunction with the 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS 2018). Our goal is to advance the state-of-the-art detection and tracking algorithms and provide a comprehensive performance evaluation for them. We evaluate 5 submitted detection and 7 submitted tracking methods on the large-scale UA-DETRAC benchmark, and the results are shared publicly on the website http://detrac-db. rit.albany.edu. We expect this challenge to advance the research and development of new detection and tracking methods for transportation applications