Car recognition using SIFT and Color Histogram

Scale invariant feature transform (SIFT) method is invariant to scale, rotation and also is partially invariant to illumination differences and noise which makes it well suited for object recognition. However, employing the SIFT features is time consuming while using a large dataset, which results from complexities of SIFT descriptor matching procedure. The following paper proposes a hierarchical method to recognize a car among 600 random samples from 40 different cars from video streams, based on colour histogram and SIFT features. The proposed method can properly recognize 85% of the cars in the database and decreases the process time up to 47% compared with the time that is needed to recognize only with SIFT. Furthermore, using sequence of frames lets the system work properly in different scenes.

Tags: Car recognition,, ImageProcessing, Compter Vision, HSL


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