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Dec 26, 2023 · Video anomaly detection (VAD) aims to identify the events that diverge from expected behavior, which has been extensively researched in recent ...
Dec 26, 2023 · We propose a Memory-Augmented Spatial-Temporal Consistency Network, aiming to model the latent consistency between spatial appearance and temporal motion.
Video anomaly detection (VAD) in intelligent surveillance systems is a crucial yet highly challenging task. Since appearance and motion information is vital ...
We propose a memory-augmented appearance-motion network (MAAM-Net) for video anomaly detection. · A margin-based latent loss is introduced to improve the ...
Video anomaly detection (VAD) is a critical task in surveil- lance video. It has been studied for many years but remains unsolved due to the difficulties and ...
We propose a spatiotemporal consistency-enhanced network (STCEN) to highlight the disturbances of abnormal data from both spatial and temporal aspects.
Experimental results on three benchmarks demonstrate the effectiveness of the spatial-temporal consistency for VAD tasks. Our method performs comparably to the ...
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Sep 26, 2024 · We propose an effective memory method for VAD, called VideoPatchCore. Inspired by PatchCore, our approach introduces a structure that prioritizes memory ...
This work proposes an Appearance-Motion Memory Consistency Network (AMMC-Net), which first makes full use of the prior knowledge of appearance and motion ...
Jul 27, 2022 · We propose spatial-temporal memories augmented auto-encoder ... network for video anomaly detection,” in Proceedings of the AAAI ...