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

3 Jan 2021  ·  Bruno Degardin, Hugo Proença ·

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

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Semi-supervised Anomaly Detection UBI-Fights SS-Model + WS-Model + Sultani et al. AUC 0.846 # 1
Decidability 1.108 # 1
EER 0.216 # 1
Semi-supervised Anomaly Detection UBI-Fights SS-Model AUC 0.819 # 2
Decidability 0.986 # 2
EER 0.284 # 2


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