The detection and localization of objects on point cloud provided by LiDAR sensors have various applications, especially for autonomous driving. As methods are being more efficient in terms of performance and execution speed, they operate in an end-to-end fashion, not allowing in case of errors the evaluation of which areas could cause failures. We propose in this paper a simple method that delivers from a simple statistical voxel encoding an intermediate top view heatmap that illustrates the global state of the scene seen by the network. Completely detected vehicles are represented on this map as elliptic blobs. Difficult cases such as occluded cars may not illustrate a complete spot, however their mark may still indicate that a possible hazard, allowing planification algorithms to decide a reduction of the velocity as something may suddenly appear. Furthermore, thanks to the alternative representation of objects, detections in terms of bounding boxes can be extracted even from the intermediate map. In addition to its implementation simplicity, the proposed architecture reaches high level of performance on the KITTI detection benchmark.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Detection based on Deep Learning Heatmap Estimation


    Contributors:


    Publication date :

    2019-06-01


    Size :

    964791 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    HOME: Heatmap Output for future Motion Estimation

    Gilles, Thomas / Sabatini, Stefano / Tsishkou, Dzmitry et al. | IEEE | 2021


    Reconfigurable avionics systems heatmap

    WONG JASON L | European Patent Office | 2022

    Free access

    Reconfigurable Avionics Systems Heatmap

    WONG JASON L | European Patent Office | 2021

    Free access

    GENERATION OF INDOOR LIKELIHOOD HEATMAP

    BEHROOZ KHORASHADI / RAJARSI GUPTA / SAUMITRA MOHAN DAS et al. | European Patent Office | 2016

    Free access

    Low-Level Sensor Fusion for 3D Vehicle Detection Using Radar Range-Azimuth Heatmap and Monocular Image

    Kim, Jinhyeong / Kim, Youngseok / Kum, Dongsuk | British Library Conference Proceedings | 2021