Recognition of Abnormal Events in Surveillance Videos using Weakly Supervised Dual-Encoder Models
avishaiAbstract
A dual-backbone framework using convolutional and transformer representations with top-k pooling detects anomalies in surveillance videos with 90.7% AUC on UCF-Crime.
We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset.
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We address the challenge of detecting rare and diverse anomalies in surveillance videos using only video-level supervision. Our dual-backbone framework combines convolutional and transformer representations through top-k pooling, achieving 90.7% area under the curve (AUC) on the UCF-Crime dataset.
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