Hog Dalal Triggs

Histograms of Oriented Gradients for Human Detection
Navneet Dalal and Bill Triggs INRIA Rhˆ ne-Alps, 655 avenue de l’Europe, Montbonnot 38334, France o {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr Abstract
We study the question of feature sets for robust visual object recognition, adopting linear SVM based human detection as a test case. After reviewing existing edge andgradient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization inoverlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds. We briefly discuss previous work on human detection in §2, give an overview of our method §3, describe ourdata sets in §4 and give a detailed description and experimental evaluation of each stage of the process in §5–6. The main conclusions are summarized in §7.

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Previous Work

1 Introduction
Detecting humans in images is a challenging task owing to their variable appearance and the wide range of poses that they can adopt. The first need is a robust feature set that allows the human form tobe discriminated cleanly, even in cluttered backgrounds under difficult illumination. We study the issue of feature sets for human detection, showing that locally normalized Histogram of Oriented Gradient (HOG) descriptors provide excellent performance relative to other existing feature sets including wavelets [17,22]. The proposed descriptors are reminiscent of edge orientation histograms [4,5],SIFT descriptors [12] and shape contexts [1], but they are computed on a dense grid of uniformly spaced cells and they use overlapping local contrast normalizations for improved performance. We make a detailed study of the effects of various implementation choices on detector performance, taking “pedestrian detection” (the detection of mostly visible people in more or less upright poses) as a testcase. For simplicity and speed, we use linear SVM as a baseline classifier throughout the study. The new detectors give essentially perfect results on the MIT pedestrian test set [18,17], so we have created a more challenging set containing over 1800 pedestrian images with a large range of poses and backgrounds. Ongoing work suggests that our feature set performs equally well for other shape-basedobject classes. 1

There is an extensive literature on object detection, but here we mention just a few relevant papers on human detection [18,17,22,16,20]. See [6] for a survey. Papageorgiou et al [18] describe a pedestrian detector based on a polynomial SVM using rectified Haar wavelets as input descriptors, with a parts (subwindow) based variant in [17]. Depoortere et al give an optimizedversion of this [2]. Gavrila & Philomen [8] take a more direct approach, extracting edge images and matching them to a set of learned exemplars using chamfer distance. This has been used in a practical real-time pedestrian detection system [7]. Viola et al [22] build an efficient moving person detector, using AdaBoost to train a chain of progressively more complex region rejection rules based onHaar-like wavelets and space-time differences. Ronfard et al [19] build an articulated body detector by incorporating SVM based limb classifiers over 1st and 2nd order Gaussian filters in a dynamic programming framework similar to those of Felzenszwalb & Huttenlocher [3] and Ioffe & Forsyth [9]. Mikolajczyk et al [16] use combinations of orientationposition histograms with binary-thresholded gradient...