skip to main content
10.1145/3191442.3191447acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicigpConference Proceedingsconference-collections
research-article

Rapid Ground Car Detection on Aerial Infrared Images

Published: 24 February 2018 Publication History

Abstract

With extensive applications of unmanned aircraft vehicle and infrared imagery's particular characteristic, ground car detections using infrared aerial images have been gradually applied to intelligent video surveillance. However, the aerial infrared images are always low-resolution and fuzzy, ground car detection is subjected to pose variations, view changes as well as surrounding radiations, this inevitably poses many challenges to detection task. In this paper, we present a novel approach toward ground car detection on infrared images via an end to end regressive neural network, other than background segmentation or foreground extraction. The main works of our research can be divided into three parts: (1) A unique aerial moving platform is built to collect a large amount of infrared images. It is achieved by assembling the DJI M-100 UAV and the FLTR TAU2 infrared sensor; (2) An aerial infrared car data set is unprecedentedly constructed. It is can be used for the following researches in this field; (3) A ground car detection model is trained. It can work in the moving and stationary cars in some severe environments. We test it on some low-resolution infrared images in a typical urban complicated environment and compare it with a state-of-the-art method. Experimental results demonstrate that the proposed approach instantly detects cars while keeping a low leak and false alarm ratio.

References

[1]
Bastian Bohn, Jochen Garcke, Rodrigo Iza-Teran, Alexander Paprotny, Benjamin Peherstorfer, Ulf Schepsmeier, and Clemens August Thole. Analysis of car crash simulation data with nonlinear machine learning methods. Procedia Computer Science, 18(1):621--630, 2013.
[2]
Hong Tai Chen, Yuan Ping Zhou, and Chang MingDeng. Study and implementation of car video surveillance system. Communications Technology, 2012.
[3]
Zehang Sun, G Bebis, and R Miller. On-road vehicle detection using gabor filters and support vector machines. In International Conference on Digital Signal Processing, pages 1019--1022 vol.2, 2002.
[4]
Pingting Luo, Fuqiang Liu, Xiaofeng Liu, and Yingqian Yang. Stationary vehicle detection in aerial surveillance with a UAV. 2012.
[5]
Yongzheng Xu, Guizhen Yu, Yunpeng Wang, Xinkai Wu, and Yalong Ma. A hybrid vehicle detection method based on viola-jones and hog + svm from uav images. Sensors, 16(8):1325, 2016.
[6]
Kwang Moo Yi, Kimin Yun, Soo Wan Kim, Hyung Jin Chang, and Young Choi Jin. Detection of moving objects with non-stationary cameras in 5.8ms: Bringing motion detection to your mobile device. In IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 27--34, 2013.
[7]
Wen Shao, Wen Yang, Gang Liu, and Jie Liu. Car detection from high-resolution aerial imagery using multiple features. In Geoscience and Remote Sensing Symposium, pages 4379--4382, 2012.
[8]
S. Ren, K. He, R Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis Machine Intelligence, PP (99):1--1, 2015.
[9]
https://github.com/shanxiliuxiaofei/NPUMVision/tree/master/Aerial%20Infrared%20Car%20Datasets.
[10]
Labelimg. https://github.com/tzutalin/labelImg.
[11]
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, and Michael Bernstein. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211--252, 2015.
[12]
P. F. Felzenszwalb, R. B. Girshick, D Mcallester, and D Ramanan. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis Machine Intelligence, 32(9):1627--45, 2010.
[13]
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. Pages 580--587, 2014.
[14]
Mark Everingham, Luc Gool, Christopher K Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2):303--338, 2010.
[15]
http://vision.cse.psu.edu/data/vividEval/datasets/PETS2005/PkTest01/index.html.
[16]
Joseph Redmon. Darknet: Open source neural networks in c. http://pjreddie.com/darknet/,2013-2016.

Cited By

View all
  • (2021)A new comparison framework to survey neural networks‐based vehicle detection and classification approachesInternational Journal of Communication Systems10.1002/dac.492834:14Online publication date: 27-Jul-2021

Index Terms

  1. Rapid Ground Car Detection on Aerial Infrared Images

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICIGP '18: Proceedings of the 2018 International Conference on Image and Graphics Processing
    February 2018
    183 pages
    ISBN:9781450363679
    DOI:10.1145/3191442
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    In-Cooperation

    • Wuhan Univ.: Wuhan University, China

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 24 February 2018

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Aerial infrared imagery
    2. End to end regressive neural network
    3. Rapid ground car detection
    4. Unmanned aircraft vehicle

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    • ShenZhen Science and Technology Foundation
    • National Natural Science

    Conference

    ICIGP 2018

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)4
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 20 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2021)A new comparison framework to survey neural networks‐based vehicle detection and classification approachesInternational Journal of Communication Systems10.1002/dac.492834:14Online publication date: 27-Jul-2021

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media