Histogram of Oriented Gradients - Further Development

Further Development

As part of the Pascal Visual Object Classes 2006 Workshop, Dalal and Triggs presented results on applying Histogram of Oriented Gradient descriptors to image objects other than human beings, such as cars, buses, and bicycles, as well as common animals such as dogs, cats, and cows. They included with their results the optimal parameters for block formulation and normalization in each case. The image in the below reference shows some of their detection examples for motorbikes.

Then as part of the 2006 European Conference on Computer Vision, Dalal and Triggs teamed up with Cordelia Schmid to apply Histogram of Oriented Gradient detectors to the problem of human detection in films and videos. Essentially their technique involves the combination of regular HOG descriptors on individual video frames with new Internal Motion Histograms (IMH) on pairs of subsequent video frames. These Internal Motion Histograms use the gradient magnitudes from optical flow fields obtained from two consecutive frames. These gradient magnitudes are then used in the same manner as those produced from static image data within the HOG descriptor approach. When testing on two large datasets taken from several movie DVDs, the combined HOG-IMH method yielded a miss rate of approximately 0.1 at a false positive rate.

At the Intelligent Vehicles Symposium in 2006, F. Suard, A. Rakotomamonjy, and A. Bensrhair introduced a complete system for pedestrian detection based on HOG descriptors. Their system operates using two infrared cameras. Since human beings appear brighter than their surroundings on infrared images, the system first locates positions of interest within the larger view field where humans could possibly be located. Then normal Support Vector Machine classifiers operate on the HOG descriptors taken from these smaller positions of interest to formulate a decision regarding the presence of a pedestrian. Once pedestrians are located within the view field, the actual position of the pedestrian is estimated using stereovision.

At the IEEE Conference on Computer Vision and Pattern Recognition in 2006, Qiang Zhu, Shai Avidan, Mei-Chen Yeh, and Kwang-Ting Cheng presented an algorithm to significantly speed up human detection using HOG descriptor methods. Their method uses HOG descriptors in combination with the cascade of rejecters algorithm normally applied with great success to the problem of face detection. Also, rather than relying on blocks of uniform size, they introduce blocks that vary in size, location, and aspect ratio. In order to isolate the blocks best suited for human detection, they applied the AdaBoost algorithm to select those blocks to be included in the rejecter cascade. In their experimentation, their algorithm achieved comparable performance to the original Dalal and Triggs algorithm, but operated at speeds up to 70 times faster. In April 2006, the Mitsubishi Electric Research Laboratories applied for the U.S. Patent of this algorithm under application number 20070237387.