Traffic scene understanding involves the perception and accurate representation of a vehicles surroundings within a comprehensive environment model. Generally, automotive environment models incorporate multiple different and complementary representations each focusing on specific aspects of traffic scenes such as the drivable terrain, dynamic objects, the lane layout, etc. Considering camera based environment perception, multi-task models offer an efficient approach capable of generating complementary representations in an integrated manner. Towards this objective, we propose a convolutional neural network (CNN) comprised of a shared encoder stage and specific decoders for road segmentation and object detection. In comparison to exclusive models for each separate task, the redundant computation of common feature maps is avoided resulting in fast inference times even on low cost embedded systems. Furthermore, the detection stage is extended to predict the orientation of detected objects. The orientation estimate guides a consecutive 3D bounding box estimation based on analytic geometry. Thus, the CNN suffices to generate a basic environment model consisting of the spatial information of dynamic objects as well as the free space area obtained from road segmentation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes


    Contributors:


    Publication date :

    2018-11-01


    Size :

    778145 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Understanding object relations in traffic scenes

    Hensel, Irina / Bachmann, Alexander / Hummel, Britta et al. | Tema Archive | 2010


    Understanding Pedestrian Behavior in Complex Traffic Scenes

    Rasouli, Amir / Kotseruba, Iuliia / Tsotsos, John K. | IEEE | 2018


    Understanding Pedestrians’ Car-Hailing Intention in Traffic Scenes

    Wang, Zhenghao / Lian, Jing / Li, Linhui et al. | Springer Verlag | 2022



    Monitoring crowded traffic scenes

    Maurin, B. / Masoud, O. / Papanikolopoulos, N. | IEEE | 2002