Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs INRIA Rhone-Alps, �� 655 avenue de l���Europe, Montbonnot 38334, France {Navneet.Dalal,Bill.Triggs}@inrialpes.fr, http://lear.inrialpes.fr Abstract We study the question of feature sets for robust visual ob- ject recognition, adopting linear SVM based human detec- tion as a test case. After reviewing existing edge and gra- dient based descriptors, we show experimentally that grids of Histograms of Oriented Gradient (HOG) descriptors sig- nificantly outperform existing feature sets for human detec- tion. 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 in overlapping de- scriptor 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. 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 to be discriminated cleanly, even in cluttered backgrounds under difficult illumination. We study the issue of feature sets for human detection, showing that lo- cally normalized Histogram of Oriented Gradient (HOG) de- scriptors provide excellent performance relative to other ex- isting 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 im- proved 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 test case. For sim- plicity and speed, we use linear SVM as a baseline classifier throughout the study. The new detectors give essentially per- fect results on the MIT pedestrian test set [18,17], so we have created a more challenging set containing over 1800 pedes- trian images with a large range of poses and backgrounds. Ongoing work suggests that our feature set performs equally well for other shape-based object classes. We briefly discuss previous work on human detection in ��2, give an overview of our method ��3, describe our data 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. 2 Previous Work There is an extensive literature on object detection, but here we mention just a few relevant papers on human detec- tion [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 optimized version 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 pedes- trian 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 on Haar-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 orientation- position histograms with binary-thresholded gradient magni- tudes to build a parts based method containing detectors for faces, heads, and front and side profiles of upper and lower body parts. In contrast, our detector uses a simpler archi- tecture with a single detection window, but appears to give significantly higher performance on pedestrian images. 3 Overview of the Method This section gives an overview of our feature extraction chain, which is summarized in fig. 1. Implementation details are postponed until ��6. The method is based on evaluating well-normalized local histograms of image gradient orienta- tions in a dense grid. Similar features have seen increasing use over the past decade [4,5,12,15]. The basic idea is that local object appearance and shape can often be characterized rather well by the distribution of local intensity gradients or 1