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Robust vehicle detection in aerial images using bag-of-words and orientation aware scanning

Version 2 2024-06-06, 08:10
Version 1 2018-12-10, 11:05
journal contribution
posted on 2024-06-06, 08:10 authored by H Zhou, Lei WeiLei Wei, Chee Peng LimChee Peng Lim, Douglas CreightonDouglas Creighton, S Nahavandi
This paper presents a novel approach to automatically detect and count cars in different aerial images, which can be satellite or unmanned aerial vehicle (UAV) images. Variations in satellite and/or UAV data make it particularly challenging to have a robust method that works properly on a variety of images. A solution based on the bag-of-words (BoW) model is explored in this paper due to its invariance characteristic and highly stable performance in object/scene categorization. Different from categorization tasks, vehicle detection needs to localize the positions of cars in images. To make BoW suitable for this purpose, we extensively improve the methodology in three aspects, namely, by introducing a recently proposed feature representation, i.e., the local steering kernel descriptor, adding spatial structure constraints, and developing an orientation aware scanning mechanism to produce detection with 'one-window-one-car' results. Experiments are conducted on various aerial images with large variations, which consist of data from two public databases, e.g., the Overhead Imagery Research Data Set and Vehicle Detection in Aerial Imagery, as well as other satellite and UAV images. The results demonstrate the effectiveness and robustness of the proposed method. Compared with existing techniques, the proposed method is applicable to a wider range of aerial images.

History

Journal

IEEE transactions on geoscience and remote sensing

Volume

56

Pagination

7074-7085

Location

Piscataway, N.J.

ISSN

0196-2892

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2018, IEEE

Issue

12

Publisher

Institute of Electrical and Electronics Engineers