Video vehicle detection is more valuable and challenging than image vehicle detection for an intelligent transportation system. Due to the existing situation of vehicle blurring, occlusion, and scale changing in traffic monitoring, using static vehicle detection network often leads to the decrease of detection accuracy. In this paper, based on DFF method, we fuse a tracking algorithm to realize box propagation, and form a new video vehicle detection framework that considers both detection accuracy and speed. We extract feature maps in key frames, and propagate feature maps and boxes in non-key frames. Compared with static detectors, the proposed method greatly improves the consistency of video vehicle detection results. In addition, the detection performance of our method is obviously superior to the basic detector DFF in accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Feature and Box Propagation for Video Vehicle Detection


    Contributors:
    Yang, Yanni (author) / Song, Huansheng (author) / Dai, Zhe (author) / Zhang, Wentao (author) / Chen, Yan (author)

    Conference:

    20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)


    Published in:

    CICTP 2020 ; 777-788


    Publication date :

    2020-12-09




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Video Vehicle Detection Based on Local Feature

    Qian, Zhi Ming ;Shi, Hong Xing ;Yang, Jia Kuan | Trans Tech Publications | 2011




    Vehicle feature availability detection

    BENMIMOUN AHMED / MA CHENHAO / PAK TONY TAE-JIN et al. | European Patent Office | 2023

    Free access

    Vehicle feature availability detection

    BEN MIMOUN AHMED / MA CHENHAO / PARK TONY TAE-JIN et al. | European Patent Office | 2023

    Free access