Vehicular Trajectory Classification and Traffic Anomaly Detection in Videos Using a Hybrid CNN-VAE Architecture

Visual surveillance has become indispensable in the evolution of Intelligent Transportation Systems (ITS). Video object trajectories are key to many of the visual surveillance applications. Classifying varying length time series data such as video object trajectories using conventional neural networ...

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Veröffentlicht in:	IEEE transactions on intelligent transportation systems Jg. 23; H. 8; S. 11891 - 11902
Hauptverfasser:	Kumaran Santhosh, Kelathodi, Dogra, Debi Prosad, Roy, Partha Pratim, Mitra, Adway
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Anomalies Anomaly detection Artificial neural networks Classification Convolutional neural network Convolutional neural networks Datasets deep learning Dirichlet process mixture model Feature extraction Image color analysis Intelligent transportation systems Movement Moving object recognition Neural networks Representations Surveillance traffic anomaly detection Traffic speed Training data Trajectories Trajectory trajectory classification Transportation networks variational autoencoder Videos visual surveillance
ISSN:	1524-9050, 1558-0016
Online-Zugang:	Volltext
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