Human Activity Analysis: Iterative Weak/Self-Supervised Learning Frameworks for Detecting Abnormal Events

Abstract

Having observed the unsatisfactory state-of-the-art performance in detecting abnormal events, this paper describes an iterative self-supervised learning method for such purpose. The proposed solution is composed of two experts that - at each step - find the most confidently classified instances to augment the amount of data available for the next iteration. Our contributions are four-fold: 1) we describe the iterative learning framework composed of experts working in the weak/self-supervised paradigms and providing learning data to each other, with the novel instances being filtered by a Bayesian framework; 2) upon Sultani et al. [14]'s work, we suggest a novel term the loss function that spreads the scores in the unit interval and is important for the performance of the iterative framework; 3) we propose a late decision fusion scheme, in which an ensemble of Decision Trees learned from bootstrap samples fuses the scores of the top-3 methods, reducing the EER values about 20% over the state-of-the-art; and 4) we announce the “Fights” dataset, fully annotated at the frame level, that can be freely used by the research community. The code, details of the experimental protocols and the dataset are publicly available at http://github.com/DegardinBruno/.