Wide Area Motion Imagery (WAMI) are usually taken from unmaned air vehicles at low frame rates, and having very wide ground coverage. These images serve as rich source for many applications like surveillance, urban planing and traffic monitoring. Thus, understanding WAMI imagery exploitation has been gaining more interest recent years. In this paper, we focus on estimating the pose of vehicles in WAMI imagery. The difficulty of this task lies in that a vehicle only occupies a very small low-contrast region with confusing visual appearance in a WAMI image, which raises a serious problem for conventional approaches based on low-level image cues or priors. In this paper, we tackle this problem by adopting deep learning approach, using deep Convolutional Neural Networks (CNN) to learn the pose of vehicles in WAMI images. The proposed deep convolutional network based pose estimation exceeds baseline by 31.5%. Furthermore, we analyzed the effect of different level of context information on the estimation accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle pose estimation in WAMI imagery via deep convolutional neural networks


    Contributors:
    Yi, Meng (author) / Wang, Dong (author) / Yang, Fan (author) / Xu, Jonathan (author) / Cai, Yiran (author) / Blasch, Erik (author) / Sheaff, Carolyn (author) / Chen, Genshe (author) / Ling, Haibin (author)


    Publication date :

    2016-07-01


    Size :

    930810 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Vehicle classification in WAMI imagery using deep network

    Yi, Meng / Yang, Fan / Blasch, Erik et al. | SPIE | 2016


    Vehicle classification in WAMI imagery using deep network [9838-13]

    British Library Conference Proceedings | 2016


    Elimination of resampling errors in wide area motion imagery (WAMI)

    Cohenour, Curtis / Rovito, Todd / Van Graas, Frank | IEEE | 2017


    Ensemble convolutional neural networks for pose estimation

    Kawana, Yuki / Ukita, Norimichi / Huang, Jia-Bin et al. | British Library Online Contents | 2018