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

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

3 Jan 2021archive 2025-07-28

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 http://github.com/DegardinBruno/.

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DegardinBruno/human_self_learning_anomaly officialtf report

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Tasks

Abnormal Event Detection In Video Anomaly Detection Anomaly Detection In Surveillance Videos Classification Data Augmentation General Classification Self-Supervised Anomaly Detection Self-Supervised Learning Semi-Supervised Video Classification Semi-supervised Anomaly Detection Task 2

Datasets

Introduced by this paper, per the archive.

UBI-Fights

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Semi-supervised Anomaly Detection	UBI-Fights	SS-Model + WS-Model + Sultani et al.	AUC	0.846	#1 of 7	Archive leaderboard	report
Semi-supervised Anomaly Detection	UBI-Fights	SS-Model + WS-Model + Sultani et al.	Decidability	1.108	#1 of 7	Archive leaderboard	report
Semi-supervised Anomaly Detection	UBI-Fights	SS-Model + WS-Model + Sultani et al.	EER	0.216	#1 of 7	Archive leaderboard	report
Semi-supervised Anomaly Detection	UBI-Fights	SS-Model	AUC	0.819	#2 of 7	Archive leaderboard	report
Semi-supervised Anomaly Detection	UBI-Fights	SS-Model	Decidability	0.986	#2 of 7	Archive leaderboard	report
Semi-supervised Anomaly Detection	UBI-Fights	SS-Model	EER	0.284	#2 of 7	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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