An Efficient Approach for Anomaly Detection in Traffic Videos

• URL: http://arxiv.org/abs/2104.09758v1
• Date: Tue, 20 Apr 2021 04:43:18 GMT
• Title: An Efficient Approach for Anomaly Detection in Traffic Videos
• Authors: Keval Doshi, Yasin Yilmaz
• Abstract summary: We propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices. The proposed approach comprises a pre-processing module that detects changes in the scene and removes the corrupted frames. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic.
• Score: 30.83924581439373
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Due to its relevance in intelligent transportation systems, anomaly detection in traffic videos has recently received much interest. It remains a difficult problem due to a variety of factors influencing the video quality of a real-time traffic feed, such as temperature, perspective, lighting conditions, and so on. Even though state-of-the-art methods perform well on the available benchmark datasets, they need a large amount of external training data as well as substantial computational resources. In this paper, we propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices, e.g., on a roadside camera. The proposed approach comprises a pre-processing module that detects changes in the scene and removes the corrupted frames, a two-stage background modelling module and a two-stage object detector. Finally, a backtracking anomaly detection algorithm computes a similarity statistic and decides on the onset time of the anomaly. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic. Experimental results on the Track 4 test set of the 2021 AI City Challenge show the efficacy of the proposed framework as we achieve an F1-score of 0.9157 along with 8.4027 root mean square error (RMSE) and are ranked fourth in the competition.

Related papers

• DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
  Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving. Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed. We introduce DARTH, a holistic test-time adaptation framework for MOT.
  arXiv  Detail & Related papers  (2023-10-03T10:10:42Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• Real-Time Driver Monitoring Systems through Modality and View Analysis [28.18784311981388]
  Driver distractions are known to be the dominant cause of road accidents. State-of-the-art methods prioritize accuracy while ignoring latency. We propose time-effective detection models by neglecting the temporal relation between video frames.
  arXiv  Detail & Related papers  (2022-10-17T21:22:41Z)
• Real-Time Accident Detection in Traffic Surveillance Using Deep Learning [0.8808993671472349]
  This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method. The robustness of the proposed framework is evaluated using video sequences collected from YouTube with diverse illumination conditions.
  arXiv  Detail & Related papers  (2022-08-12T19:07:20Z)
• Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing [42.03797195839054]
  We propose a dual-modality modularized methodology for the robust detection of abnormal vehicles. For the vehicle detection and tracking module, we adopted YOLOv5 and multi-scale tracking to localize the anomalies. Experiments conducted on the Track 4 testset of the NVIDIA 2021 AI City Challenge yielded a result of 0.9302 F1-Score and 3.4039 root mean square error (RMSE)
  arXiv  Detail & Related papers  (2021-06-09T12:04:25Z)
• 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)
• Unsupervised Feature Learning for Event Data: Direct vs Inverse Problem Formulation [53.850686395708905]
  Event-based cameras record an asynchronous stream of per-pixel brightness changes. In this paper, we focus on single-layer architectures for representation learning from event data. We show improvements of up to 9 % in the recognition accuracy compared to the state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-23T10:40:03Z)
• A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video [120.18562044084678]
  Abnormal event detection in video is a complex computer vision problem that has attracted significant attention in recent years. We propose a background-agnostic framework that learns from training videos containing only normal events.
  arXiv  Detail & Related papers  (2020-08-27T18:39:24Z)
• Anomalous Motion Detection on Highway Using Deep Learning [14.617786106427834]
  This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods.
  arXiv  Detail & Related papers  (2020-06-15T05:40:11Z)
• Tracking Road Users using Constraint Programming [79.32806233778511]
  We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem. Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.
  arXiv  Detail & Related papers  (2020-03-10T00:04:32Z)
• A Video Analysis Method on Wanfang Dataset via Deep Neural Network [8.485930905198982]
  We describe the new function for real-time multi-object detection in sports competition and pedestrians flow detection in public based on deep learning. Based on the proposed algorithm, we adopt wanfang sports competition dataset as the main test dataset. Our work also can used for pedestrians flow detection and pedestrian alarm tasks.
  arXiv  Detail & Related papers  (2020-02-28T04:09:53Z)

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.