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Automatic localization of target objects in digital images is an important task in Computer Vision. The Generalized Hough Transform (GHT) and its variant, the Discriminative Generalized Hough Transform (DGHT), are model-based object localization algorithms which determine the most likely object position based on accumulated votes in the so-called Hough space. Many automatic localization algorithms-including the GHT and the DGHT-operate on edge images, using e.g. the Canny or the Sobel Edge Detector. However, if the image contains many edges not belonging to the object of interest (e.g. from other objects, background clutter, noise etc.), these edges cause misleading votes which increase the probability of localization errors. In this paper we investigate the effect of a more sophisticated edge detection algorithm, called Structured Edge Detector, on the performance of a DGHT-based object localization approach. This method utilizes information on the shape of the target object to substantially reduce the amount of non-object edges. Combining this technique with the DGHT leads to a significant localization performance improvement for automatic pedestrian and car detection.

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