Papers › 3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos

3D ResNet with Ranking Loss Function for Abnormal Activity Detection in Videos

4 Feb 2020arXiv:2002.01132archive 2025-07-28

Shikha Dubey, Abhijeet Boragule, Moongu Jeon

Abnormal activity detection is one of the most challenging tasks in the field of computer vision. This study is motivated by the recent state-of-art work of abnormal activity detection, which utilizes both abnormal and normal videos in learning abnormalities with the help of multiple instance learning by providing the data with video-level information. In the absence of temporal-annotations, such a model is prone to give a false alarm while detecting the abnormalities. For this reason, in this paper, we focus on the task of minimizing the false alarm rate while performing an abnormal activity detection task. The mitigation of these false alarms and recent advancement of 3D deep neural network in video action recognition task collectively give us motivation to exploit the 3D ResNet in our proposed method, which helps to extract spatial-temporal features from the videos. Afterwards, using these features and deep multiple instance learning along with the proposed ranking loss, our model learns to predict the abnormality score at the video segment level. Therefore, our proposed method 3D deep Multiple Instance Learning with ResNet (MILR) along with the new proposed ranking loss function achieves the best performance on the UCF-Crime benchmark dataset, as compared to other state-of-art methods. The effectiveness of our proposed method is demonstrated on the UCF-Crime dataset.

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Tasks

Action Detection Action Recognition Activity Detection Anomaly Detection Anomaly Detection In Surveillance Videos Multiple Instance Learning Temporal Action Localization

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection In Surveillance Videos	UCF-Crime	MILR	Decidability	-	#18 of 21	Archive leaderboard	report
Anomaly Detection In Surveillance Videos	UCF-Crime	MILR	EER	-	#18 of 21	Archive leaderboard	report
Anomaly Detection In Surveillance Videos	UCF-Crime	MILR	ROC AUC	76.67	#18 of 21	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.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Global Average Pooling Max Pooling ReLU Residual Block Residual Connection

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