Iterative weak/self-supervised classification framework for abnormal events detection

Bruno Degardin

doi:10.1016/j.patrec.2021.01.031

2021-05-01

PRE

Bruno Degardin

Hugo Proença *

DOI：10.1016/j.patrec.2021.01.031

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摘要

En 中文

The detection of abnormal events in surveillance footage remains a challenge and has been the scope of various research works. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with four-fold contributions: 1) upon the work of Sultani et al., we introduce one iterative learning framework composed of two experts working in the weak and self-supervised paradigms and providing additional amounts of learning data to each other, where the novel instances at each iteration are filtered by a Bayesian framework that supports the iterative data augmentation task; 2) we describe a novel term that is added to the baseline loss to spread the scores in the unit interval, which is crucial for the performance of the iterative framework; 3) we propose a Random Forest ensemble that fuses at the score level the top performing methods and reduces the EER values about 20% over the state-of-the-art; and 4) we announce the availability of the UBI-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/ . (c) 2021 Elsevier B.V. All rights reserved.

Keyword:

Visual surveillance

Abnormal events detection

Weakly supervised learning