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.

Image analysis and computer vision: 1991

Abstract: This paper presents a bibliography of nearly 1200 references related to computer vision and image analysis, arranged by subject matter. The topics covered include architectures; computational techniques; feature detection and segmentation; image analysis; two-dimensional shape; pattern; color and texture; matching and stereo; three-dimensional recovery and analysis; three-dimensional shape; and motion. A few references are also given on related topics, such as geometry, graphics, image input/output and coding, image processing, optical processing, visual perception, neural nets, pattern recognition and artificial intelligence, as well as on applications.

Framework for dynamic hand gesture recognition using Grassmann manifold for intelligent vehicles

Abstract: In this study, the authors propose a novel and robust approach to control auxiliary tasks in vehicles using hand gestures. First, they create a three-dimensional video volume by appending one frame to other that captures the motion history of frames. Then, they extract features using histogram of oriented gradients on each video volume. These features are represented in the form of subspaces on Grassmann manifold. To improve the recognition accuracy, they map the data from one manifold to another manifold with the help of a Grassmann kernel. Grassmann graph embedding discriminant analysis framework is used to classify the gestures. They perform experiments on two datasets: LISA and Cambridge Hand Gesture in three different testing methods such as 1/3-subject, 2/3-subject and cross-subject. Experimental results show that their proposed model outperforms and is comparable with the state-of-the-art methods.

FPGA implementation of HOG based multi-scale pedestrian detection

Abstract: Pedestrian detection is needed for many vison applications including surveillance, Advanced Driver Assistance Systems (ADAS), Intelligent Transport System (ITS), drone, robotics, etc. There are different sizes of pedestrian or human in an image due to the different distances from the camera and different object's height. To detect all of the objects with different sizes, a multi-scale detector is needed. In this study, a multi-scale pedestrian detection is designed based on histogram of oriented gradients (HOG) and implement the method on a field programmable gate array (FPGA). The processing includes three stages: the input color image is converted to a gray one and then down sampled the gray image by 2 and by 4, respectively. different window sizes are used to extract the features of HOG from three size gray images. Final, linear support vector machine (SVM) is used to classify the extracted features for different window sizes. The experimental results show that the system costs 94,374 logic elements, which is about 82% of total logic elements s of Terasic DE2-115 development board. The system detection accuracy is about 97% on average and the processing speed can achieve 60 fps for 640×480 resolution.

British Sign Language Recognition In The Wild Based On Multi-Class SVM

Abstract: Developing assistive, cost-effective, non-invasive technologies to aid communication of people with hearing impairments is of prime importance in our society, in order to widen accessibility and inclusiveness. For this purpose, we have developed an intelligent vision system embedded on a smartphone and deployed in the wild. In particular, it integrates both computer vision methods involving Histogram of Oriented Gradients (HOG) and machine learning techniques such as multi-class Support Vector Machine (SVM) to detect and recognize British Visual Language (BSL) signs automatically. Our system was successfully tested on a real-world dataset containing 13,066 samples and shown an accuracy of over 99% with an average processing time of 170ms, thus appropriate for real-time visual signing.