Weakly supervised video anomaly detection method based on meta-learning
Video anomaly detection usually involves many unknown scenarios, and current weak supervision methods only consider the accuracy of anomaly detection and ignore the generalization ability of unknown scenarios, resulting in poor performance when the model is transferred to a new scenario.To address the generalization problem of the model, this paper proposes a meta-learning based method.The core idea of the method is to learn an adaptive model through meta-learning and make the new model adapt to a new scenario quickly by designing multiple tasks.This method builds a two-stage video anomaly detection framework.In the inner phase, the detection accuracy of the basic detector is improved by reducing the internal loss function of the task.In the outer loop phase, the model is adapted to different tasks and the internal representation of the model is improved, so that it is easy to fine-tune quickly in new scenarios.The new method improves the generalization ability of the model to unseen scenarios without reducing the accuracy of the existing method, and greatly reduces the number of iterations and training time when the model transfers to the new scenarios.The number of training iterations on UCF-Crime dataset, XD-Violence dataset and UCSD Ped2 dataset is reduced to 105, 125 and 135 rounds respectively.
video anomaly detectionmeta-learningweakly supervised learning
国家科技期刊平台
NSTL
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