Fast Vehicle Detection using Correlation Filters
- Authors
- Han, Sangpil; Kim, Minjae; Park, Seokmok; Paik, Joonki
- Issue Date
- Oct-2017
- Publisher
- 대한전자공학회
- Keywords
- Correlation filter; Object detection; Vehicle detection; Car localization
- Citation
- IEIE Transactions on Smart Processing & Computing, v.6, no.5, pp 309 - 316
- Pages
- 8
- Journal Title
- IEIE Transactions on Smart Processing & Computing
- Volume
- 6
- Number
- 5
- Start Page
- 309
- End Page
- 316
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/5351
- DOI
- 10.5573/IEIESPC.2017.6.5.309
- ISSN
- 2287-5255
- Abstract
- Object detection is very challenging research in the computer vision community, and vehicle detection has become an important issue in various applications, such as unmanned systems and intelligent transportation systems. Most of these applications require fast and accurate vehicle detection. In recent years, it has been proven that correlation filters can find target objects fast and accurately owing to Parseval’s theorem and dense sampling. However, we think that the existing correlation filters have not used all the helpful information. Therefore, we propose a robust and fast vehicle detection method based on an improved correlation filter framework that exploits the additional information from correlation filters. The proposed vehicle detection algorithm consists of five steps: i) training the correlation filter, ii) correlation of input and the trained filter, iii) finding local maxima as vehicle candidates in the correlation output, iv) filtering the candidates by using the shape and sharpness of the maxima, and v) estimation of the location and scale of the vehicles.
The proposed algorithm runs fast and accurately, so it can be applied to many other applications, such as object alignment, object detection, and object tracking. We evaluated the proposed algorithm performance by comparing it with the state-of-the-art correlation filter-based methods.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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