Look at Adjacent Frames: Video Anomaly Detection without Offline Training

• URL: http://arxiv.org/abs/2207.13798v1
• Date: Wed, 27 Jul 2022 21:18:58 GMT
• Title: Look at Adjacent Frames: Video Anomaly Detection without Offline Training
• Authors: Yuqi Ouyang, Guodong Shen, Victor Sanchez
• Abstract summary: We propose a solution to detect anomalous events in videos without the need to train a model offline. Specifically, our solution is based on a randomly-d multilayer perceptron that is optimized online to reconstruct video frames, pixel-by-pixel, from their frequency information. An incremental learner is used to update parameters of the multilayer perceptron after observing each frame, thus allowing to detect anomalous events along the video stream.
• Score: 21.334952965297667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a solution to detect anomalous events in videos without the need to train a model offline. Specifically, our solution is based on a randomly-initialized multilayer perceptron that is optimized online to reconstruct video frames, pixel-by-pixel, from their frequency information. Based on the information shifts between adjacent frames, an incremental learner is used to update parameters of the multilayer perceptron after observing each frame, thus allowing to detect anomalous events along the video stream. Traditional solutions that require no offline training are limited to operating on videos with only a few abnormal frames. Our solution breaks this limit and achieves strong performance on benchmark datasets.

Related papers

• Aggregating Long-term Sharp Features via Hybrid Transformers for Video Deblurring [76.54162653678871]
  We propose a video deblurring method that leverages both neighboring frames and present sharp frames using hybrid Transformers for feature aggregation. Our proposed method outperforms state-of-the-art video deblurring methods as well as event-driven video deblurring methods in terms of quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2023-09-13T16:12:11Z)
• Video Event Restoration Based on Keyframes for Video Anomaly Detection [9.18057851239942]
  Existing deep neural network based anomaly detection (VAD) methods mostly follow the route of frame reconstruction or frame prediction. We introduce a brand-new VAD paradigm to break through these limitations. We propose a novel U-shaped Swin Transformer Network with Dual Skip Connections (USTN-DSC) for video event restoration.
  arXiv  Detail & Related papers  (2023-04-11T10:13:19Z)
• Learning Trajectory-Aware Transformer for Video Super-Resolution [50.49396123016185]
  Video super-resolution aims to restore a sequence of high-resolution (HR) frames from their low-resolution (LR) counterparts. Existing approaches usually align and aggregate video frames from limited adjacent frames. We propose a novel Transformer for Video Super-Resolution (TTVSR)
  arXiv  Detail & Related papers  (2022-04-08T03:37:39Z)
• FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics [2.5119455331413376]
  Detection-driven real-time video analytics require continuous detection of objects contained in the video frames. Running these detectors on each and every frame in resource-constrained edge devices is computationally intensive. We propose an off-line Reinforcement Learning (RL)-based algorithm to determine these skip-lengths.
  arXiv  Detail & Related papers  (2022-03-22T07:05:57Z)
• Efficient Video Segmentation Models with Per-frame Inference [117.97423110566963]
  We focus on improving the temporal consistency without introducing overhead in inference. We propose several techniques to learn from the video sequence, including a temporal consistency loss and online/offline knowledge distillation methods.
  arXiv  Detail & Related papers  (2022-02-24T23:51:36Z)
• Memory-Augmented Non-Local Attention for Video Super-Resolution [61.55700315062226]
  We propose a novel video super-resolution method that aims at generating high-fidelity high-resolution (HR) videos from low-resolution (LR) ones. Previous methods predominantly leverage temporal neighbor frames to assist the super-resolution of the current frame. In contrast, we devise a cross-frame non-local attention mechanism that allows video super-resolution without frame alignment.
  arXiv  Detail & Related papers  (2021-08-25T05:12:14Z)
• Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories [56.91664227337115]
  We introduce a collaborative memory mechanism that encodes information across multiple sampled clips of a video at each training iteration. This enables the learning of long-range dependencies beyond a single clip. Our proposed framework is end-to-end trainable and significantly improves the accuracy of video classification at a negligible computational overhead.
  arXiv  Detail & Related papers  (2021-04-02T18:59:09Z)
• Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance [16.220077781635748]
  An enhanced model-agnostic meta-learner is trained using both the original video frames and segmented masks of track area extracted from the video. Numerical results show that the enhanced meta-learner successfully adapts unseen scene with only few newly collected video frame samples.
  arXiv  Detail & Related papers  (2020-11-09T08:59:15Z)
• Scene-Adaptive Video Frame Interpolation via Meta-Learning [54.87696619177496]
  We propose to adapt the model to each video by making use of additional information that is readily available at test time. We obtain significant performance gains with only a single gradient update without any additional parameters.
  arXiv  Detail & Related papers  (2020-04-02T02:46:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.