Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity. In this paper, we propose a pipeline for understanding vehicle behaviour from a monocular image sequence or video. A monocular sequence along with scene semantics, optical flow and object labels are used to get spatial information about the object (vehicle) of interest and other objects (semantically contiguous set of locations) in the scene. This spatial information is encoded by a Multi-Relational Graph Convolutional Network (MR-GCN), and a temporal sequence of such encodings is fed to a recurrent network to label vehicle behaviours. The proposed framework can classify a variety of vehicle behaviours to high fidelity on datasets that are diverse and include European, Chinese and Indian on-road scenes. The framework also provides for seamless transfer of models across datasets without entailing re-annotation, retraining and even fine-tuning. We show comparative performance gain over baseline Spatio-temporal classifiers and detail a variety of ablations to showcase the efficacy of the framework.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks




    Publication date :

    2020-10-19


    Size :

    2369647 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Relational Fusion Networks: Graph Convolutional Networks for Road Networks

    Jepsen, Tobias Skovgaard / Jensen, Christian S. / Nielsen, Thomas Dyhre | IEEE | 2022


    Enhancing Road Safety through Accurate Detection of Hazardous Driving Behaviors with Graph Convolutional Recurrent Networks

    Khosravinia, Pooyan / Perumal, Thinagaran / Zarrin, Javad | ArXiv | 2023

    Free access


    Multi‐receptive field graph convolutional neural networks for pedestrian detection

    Shen, Chao / Zhao, Xiangmo / Fan, Xing et al. | Wiley | 2019

    Free access

    Multi-receptive field graph convolutional neural networks for pedestrian detection

    Shen, Chao / Zhao, Xiangmo / Fan, Xing et al. | IET | 2019

    Free access