Fast Unsupervised Anomaly Detection in Traffic Videos

Doshi, Keval; Yılmaz, Yasin

doi:10.1109/cvprw50498.2020.00320

Cited by 45 publications

(23 citation statements)

References 10 publications

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“…Common vehicle behaviors in traffic systems include running red lights, legal diversions, illegal diversions, legal parking, illegal parking [15][16][17] , etc.…”

Section: Different Case Detectionmentioning

confidence: 99%

Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin

Lin

Yang

2021

J. Shanghai Jiaotong Univ. (Sci.)

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Analyzing a vehicle’s abnormal behavior in surveillance videos is a challenging field, mainly due to the wide variety of anomaly cases and the complexity of surveillance videos. In this study, a novel intelligent vehicle behavior analysis framework based on a digital twin is proposed. First, detecting vehicles based on deep learning is implemented, and Kalman filtering and feature matching are used to track vehicles. Subsequently, the tracked vehicle is mapped to a digital-twin virtual scene developed in the Unity game engine, and each vehicle’s behavior is tested according to the customized detection conditions set up in the scene. The stored behavior data can be used to reconstruct the scene again in Unity for a secondary analysis. The experimental results using real videos from traffic cameras illustrate that the detection rate of the proposed framework is close to that of the state-of-the-art abnormal event detection systems. In addition, the implementation and analysis process show the usability, generalization, and effectiveness of the proposed framework.

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“…Common vehicle behaviors in traffic systems include running red lights, legal diversions, illegal diversions, legal parking, illegal parking [15][16][17] , etc.…”

Section: Different Case Detectionmentioning

confidence: 99%

Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin

Lin

Yang

2021

J. Shanghai Jiaotong Univ. (Sci.)

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“…The third part of our experiments focuses on comparing SS-ITS against the baseline high-performance computing solutions: LoTAD (Long-term Traffic Anomaly Detection) [25], and FUAD (Fast Unsupervised Anomaly Detection) [20]. As shown in GB.…”

Section: Performance Against State-of-the-art High-performance Computingmentioning

confidence: 99%

SS-ITS: secure scalable intelligent transportation systems

et al. 2021

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This paper introduces a secure and scalable intelligent transportation and human behavior system to accurately discover knowledge from urban traffic data. The data are secured using blockchain learning technology, where the scalability is ensured by a threaded GPU. In addition, different optimizations are provided to efficiently process data on the GPU. A reinforcement deep learning algorithm is also established to merge local knowledge discovered on each site into global knowledge. To demonstrate the applicability of the proposed framework, intensive experiments have been carried out on well-known intelligent transportation and human behavior data. Our results show that our proposed framework outperforms the baseline solutions for the outlier detection use case.

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“…Aside from the appearance-based approach, some studies have analyzed traffic flow. Monteiro et al developed a WWD detection system based on analyzing the optical flow in three stages: learning, detection, and validation (9); Doshi and Yilmaz proposed a fast, unsupervised, anomaly detection approach based on the stationary objects detected in a video, where K-means clustering can subsequently be applied to identify potential anomalous regions (10). Fu et al proposed a hierarchical clustering framework to classify vehicle trajectories generated from motion information; anomalous trajectories can be detected based on the trajectory results (11).…”

mentioning

confidence: 99%

Identifying Wrong-Way Driving Incidents from Regular Traffic Videos Using Unsupervised Trajectory-based Method

Chang

Ren

Zhou

et al. 2021

Transportation Research Record

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Currently, transportation agencies have implemented different wrong-way driving (WWD) detection systems based on loop detectors, radar detectors, or thermal cameras. Such systems are often deployed at fixed locations in urban areas or on toll roads. The majority of rural interchange terminals does not have real-time detection systems for WWD incidents. Portable traffic cameras are used to temporarily monitor WWD activities at rural interchange terminals. However, it has always been a time-consuming task to manually review those videos to identify WWD incidents. The objective of this study was to develop an unsupervised trajectory-based method to automatically detect WWD incidents from regular traffic videos (not limited by mounting height and angle). The principle of the method includes three primary steps: vehicle recognition and trajectory generation, trajectory clustering, and outlier detection. This study also developed a new subtrajectory-based metric that makes the algorithm more adaptable for vehicle trajectory classification in different road scenarios. Finally, the algorithm was tested by analyzing 357 h of traffic videos from 14 partial cloverleaf interchange terminals in seven U.S. states. The results suggested that the method could identify all the WWD incidents in the testing videos with an average precision of 80%. The method significantly reduced person-hours for reviewing the traffic videos. Furthermore, the new method could also be applied in detecting and extracting other kinds of abnormal traffic activities, such as illegal U-turns.

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Fast Unsupervised Anomaly Detection in Traffic Videos

Cited by 45 publications

References 10 publications

Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin

Intelligent Analysis of Abnormal Vehicle Behavior Based on a Digital Twin

SS-ITS: secure scalable intelligent transportation systems

Identifying Wrong-Way Driving Incidents from Regular Traffic Videos Using Unsupervised Trajectory-based Method

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