Real-time vehicle color identification using symmetrical SURFs and chromatic strength

Abstract

This paper proposes a new vehicle color classification scheme to identify vehicles with their colors. To detect vehicles from roads, the paper proposes a novel symmetrical descriptor to determine the ROI of each vehicle without using any motion features. This scheme provides two advantages; there is no need of background subtraction and it is extremely efficient for real-time applications. After detection, a novel color-correction technique is proposed to reduce the color changes of vehicles so that vehicles can be more accurately identified. The major challenge in vehicle color identification is there are many shade (or confused) colors among vehicles. This paper proposes a new concept that the vehicles with different chromatic attributes should be separately trained even though they are in the same color category. With this concept, a novel tree-based classifier can be constructed to classify vehicles at different stages according to their chromatic strengths. The separation can significantly improve the accuracy of vehicle color classification even that vehicles are with various shade colors.

A comprehensive review of vehicle detection using computer vision

“…The speeded up robust features (SURF) is a scale and rotation invariant interest point detector and descriptor which, compared to SIFT's computational complexity, is much lower due to the substitution of Gaussian filter with a box of filters which slightly affect performance [78]. This algorithm employs a Hessian matrix approximation on integral image to local the points of interest, with second-order partial derivatives describing local curvatures [79].…”

Automated Pedestrian Recognition Based on Deep Convolutional Neural Networks

“…The improvement in recognition performance depends on the growth in feature diversity. Recently, a variety of discriminative features have been used in pedestrian detection like Scale Invariant Feature Transformation (i.e., SIFT), speeded up Robust Features (i.e., SURF), Histogram of Oriented Gradient (i.e., HOG) and Haar-like features [16], [17]. Each of them has its own limitations and drawbacks.…”