Fast Vehicle Detection using Correlation Filters
Sang-Pil Han, Minjae Kim, Seokmok Park, Joonki Paik · IEIE Transactions on Smart Processing and Computing · 2017
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.