Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.

Benchmarking haar and histograms of oriented gradients features applied to vehicle detection.

Abstract: This paper provides a comparison between two of the most used visual descriptors (image features) nowadays in the field of object detection. The investigated image features involved the Haar filters and the Histogram of Oriented Gradients (HoG) applied for the on road vehicle detection. Tests are very encouraging with a average detection of 96% on realistic on-road vehicle images.

Extended Histogram of Gradients feature for human detection

Abstract: Unsigned Histogram of Gradients (UHoG) is a popular feature used for human detection. Despite its superior performance as reported in recent literature, an inherent limitation of UHoG is that gradients of opposite directions in a cell are mapped into the same histogram bin. This is undesirable as it will produce the same UHoG feature for two different patterns. To address this problem, we propose a new feature named the Extended Histogram of Gradients (ExHoG) in this paper. It comprises two components: UHoG and a histogram of absolute bin value differences of opposite gradient directions computed from Histogram of Gradients (HoG). Our experimental results show that the proposed ExHoG consistently outperforms the standard HoG and UHoG for human detection.

Rotation-invariant object detection using Sector-ring HOG and boosted random ferns

Abstract: The histogram of oriented gradients (HOG) is widely used for image description and has proven to be very effective. In some practical applications that lack an assumption of the object’s orientation, rotation-invariant detection is of vital significance. To address this problem, this paper presents a new visual feature, Sector-ring HOG (SRHOG), which is obtained by improving the gradient binning and spatial binning based on HOG. The new feature can convert planar image rotations into cyclic shifts of the final descriptor and thereby facilitate rotated object detection. After modifying boosted random ferns in SRHOG feature domain, we further propose two strategies for rotation-invariant object detection: one depends completely on the new feature’s characteristic, and the other introduces an orientation estimation step. The former is more suitable to ‘finding objects’ and the latter can provide the higher orientation estimation accuracy. Both the use of supervised learning and working in the gradient space make our approaches effective and robust. We show these properties by thorough testing on the public Freestyle Motocross dataset and our dataset for victim detection in post-disaster rescue efforts.

Improving HOG with image segmentation: application to human detection

Abstract: In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement. We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4.47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.

Automatic vehicle detection in aerial image sequences of urban areas using 3d hog features

Abstract: With the development of low cost aerial optical sensors having a spatial resolution in the range of few centimetres, the traffic monitoring by plane receives a new boost. The gained traffic data are very useful in various fields. Near real-time applications in the case of traffic management of mass events or catastrophes and non time critical applications in the wide field of general transport planning are considerable. A major processing step for automatically provided traffic data is the automatic vehicle detection. In this paper we present a new processing chain to improve this task. First achievement is limiting the search space for the detector by applying a fast and simple pre-processing algorithm. Second achievement is generating a reliable detector. This is done by the use of HoG features (Histogram of Oriented Gradients) and their appliance on two consecutive images. A smart selection of this features and their combination is done by the Real AdaBoost (Adaptive Boosting) algorithm. Our dataset consists of images from the 3K camera system acquired over the city of Munich, Germany. First results show a high detection rate and good reliability.