Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance

• URL: http://arxiv.org/abs/2011.04254v1
• Date: Mon, 9 Nov 2020 08:59:15 GMT
• Title: Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance
• Authors: Xiao Gong, Xi Chen, Wei Chen
• Abstract summary: 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.
• Score: 16.220077781635748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video surveillance is gaining increasing popularity to assist in railway intrusion detection in recent years. However, efficient and accurate intrusion detection remains a challenging issue due to: (a) limited sample number: only small sample size (or portion) of intrusive video frames is available; (b) low inter-scene dissimilarity: various railway track area scenes are captured by cameras installed in different landforms; (c) high intra-scene similarity: the video frames captured by an individual camera share a same backgound. In this paper, an efficient few-shot learning solution is developed to address the above issues. In particular, an enhanced model-agnostic meta-learner is trained using both the original video frames and segmented masks of track area extracted from the video. Moreover, theoretical analysis and engineering solutions are provided to cope with the highly similar video frames in the meta-model training phase. The proposed method is tested on realistic railway video dataset. Numerical results show that the enhanced meta-learner successfully adapts unseen scene with only few newly collected video frame samples, and its intrusion detection accuracy outperforms that of the standard randomly initialized supervised learning.

Related papers

• Practical Video Object Detection via Feature Selection and Aggregation [18.15061460125668]
  Video object detection (VOD) needs to concern the high across-frame variation in object appearance, and the diverse deterioration in some frames. Most of contemporary aggregation methods are tailored for two-stage detectors, suffering from high computational costs. This study invents a very simple yet potent strategy of feature selection and aggregation, gaining significant accuracy at marginal computational expense.
  arXiv  Detail & Related papers  (2024-07-29T02:12:11Z)
• AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection [53.448283629898214]
  The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilize visual modality or audio modality. We propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation.
  arXiv  Detail & Related papers  (2023-10-19T19:01:26Z)
• 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)
• Anomaly Crossing: A New Method for Video Anomaly Detection as Cross-domain Few-shot Learning [32.0713939637202]
  Video anomaly detection aims to identify abnormal events that occurred in videos. Most previous approaches learn only from normal videos using unsupervised or semi-supervised methods. We propose a new learning paradigm by making full use of both normal and abnormal videos for video anomaly detection.
  arXiv  Detail & Related papers  (2021-12-12T20:49:38Z)
• PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos [20.949656274807904]
  We propose a novel yet simple algorithm called Pseudo-versa-Adrial Training (PAT) to detect the adversarial frames in a video without requiring knowledge of the attack. Experimental results on UCF-101 and 20BN-Jester datasets show that PAT can detect the adversarial video frames and videos with a high detection rate.
  arXiv  Detail & Related papers  (2021-09-13T04:05:46Z)
• Few-Shot Learning for Video Object Detection in a Transfer-Learning Scheme [70.45901040613015]
  We study the new problem of few-shot learning for video object detection. We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects.
  arXiv  Detail & Related papers  (2021-03-26T20:37:55Z)
• Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction [61.17654438176999]
  We propose a novel and robust unsupervised video anomaly detection method by frame prediction with proper design. Our proposed method obtains the frame-level AUROC score of 88.3% on the CUHK Avenue dataset.
  arXiv  Detail & Related papers  (2020-11-05T11:34:12Z)
• Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos [82.02074241700728]
  In this paper, we present a prohibitive-level action recognition model that is trained with only video-frame labels. Our method per person detectors have been trained on large image datasets within Multiple Instance Learning framework. We show how we can apply our method in cases where the standard Multiple Instance Learning assumption, that each bag contains at least one instance with the specified label, is invalid.
  arXiv  Detail & Related papers  (2020-07-21T10:45:05Z)
• TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model [51.14840210957289]
  Multi-object tracking is a fundamental vision problem that has been studied for a long time. Despite the success of Tracking by Detection (TBD), this two-step method is too complicated to train in an end-to-end manner. We propose a concise end-to-end model TubeTK which only needs one step training by introducing the bounding-tube" to indicate temporal-spatial locations of objects in a short video clip.
  arXiv  Detail & Related papers  (2020-06-10T06:45:05Z)

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.