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 networks, can be challenging. In this paper, we propose trajectory classification and anomaly detection using a hybrid Convolutional Neural Network (CNN) and Variational Autoencoder (VAE) architecture. First, we introduce a high level features for varying length object trajectories using color gradient representation. In the next stage, a semi-supervised way to annotate moving object trajectories extracted using Temporally Incremental Gravitational Model (TIGM) is used for class labeling. For training, anomalous trajectories are identified using t-Distributed Stochastic Neighbor Embedding (t-SNE). Finally, a hybrid CNN-VAE architecture has been proposed for trajectory classification and anomaly detection. The results obtained using publicly available surveillance video datasets reveal that the proposed method can successfully identify traffic anomalies such as violations in lane driving, sudden speed variations, abrupt termination of vehicle during movement, and vehicles moving in wrong directions. The accuracy of trajectory classification improves by a margin of 1-6% against popular neural networks-based classifiers across various datasets using the proposed high-level features. The gradient representation also improves the anomaly detection accuracy significantly (30-35%). Code and dataset can be found at https://github.com/santhoshkelathodi/CNN-VAE.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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




    Publication date :

    2022-08-01


    Size :

    10068481 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    A Three-Stage Anomaly Detection Framework for Traffic Videos

    Junzhou Chen / Jiancheng Wang / Jiajun Pu et al. | DOAJ | 2022

    Free access

    A Survey on Vehicular Traffic Flow Anomaly Detection Using Machine Learning

    Chew Jackel Vui Lung / Asli Mohammad Fadhli | DOAJ | 2024

    Free access

    Trajectory anomaly detection system and online trajectory anomaly detection method

    LI WENBIN / YAO DI / BI JINGPING | European Patent Office | 2024

    Free access

    AI-Empowered Speed Extraction via Port-Like Videos for Vehicular Trajectory Analysis

    Chen, Xinqiang / Wang, Zichuang / Hua, Qiaozhi et al. | IEEE | 2023


    Big Data architecture proposal for vehicular traffic detection

    Herrera Herrera, Nelson Ivan | IEEE | 2020