Stereo-camera-based object detection using fuzzy color histograms and a fuzzy classifier with depth and shape estimations

This paper proposes a new method of detecting an object containing multiple colors with non-homogeneous distributions in complex backgrounds and subsequently estimating the depth and shape of the object using a stereo camera. To extract features for object detection, this paper proposes fuzzy color histograms (FCHs) based on the self-splitting clustering (SSC) of the hue-saturation (HS) color space. For each scanning window in a pyramid of scaled images, the FCH is obtained by accumulating the fuzzy degrees of all of the pixels belonging to each cluster. The FCH is fed to a fuzzy classifier to detect an object in the left image captured by the stereo camera. To find the matched object region in the right image, the left and right images are first segmented using the SSC-partitioned HS space. The depth of the object is then found by performing stereo matching on the segmented images. To find the shape of the object, a disparity map is built using the estimated object depth to automatically determine the stereo matching window size and disparity search range. Finally, the shape of the object is segmented from the disparity map. The experimental results of the detection of different objects with depth and shape estimations are used to verify the performance of the proposed method. Comparisons with different detection and disparity map construction methods are performed to demonstrate the advantage of the proposed method.