A novel object detection method which combines color and scale invariant features is presented in this paper. The
detection system mainly adopts the widely used framework of SIFT (Scale Invariant Feature Transform), which consists
of both a keypoint detector and descriptor. Although SIFT has some impressive advantages, it is not only
computationally expensive, but also vulnerable to color images. To overcome these drawbacks, we employ the local
color kernel histograms and Haar Wavelet Responses to enhance the descriptor's distinctiveness and computational
efficiency. Extensive experimental evaluations show that the method has better robustness and lower computation costs.
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