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.

Linear SVM classifier based HOG car detection

Abstract: Improving safety and reducing accidents are the most goals of Advanced Driver Assistance Systems (ADAS). For their low cost and capability of providing information about driving environments, Vision-based driver assistance systems are the most important systems in recent years. In these systems, robust and precise vehicle detection is a critical step, and the detected cars can be used for various applications. This paper presents an algorithm for vehicle detection in an urban environment which is very important for driver assistance systems and autonomous driving. To succeed the detection of a vehicle, we propose the histogram of oriented gradients features descriptor (HOG) and linear support vector machine (SVM) for the classification. Our experimental results illustrate the robustness and precision of our algorithm for different scenes.

Multi-modality-based Arabic sign language recognition

Abstract: With the increase in the number of deaf-mute people in the Arab world and the lack of Arabic sign language (ArSL) recognition benchmark data sets, there is a pressing need for publishing a large-volume and realistic ArSL data set. This study presents such a data set, which consists of 150 isolated ArSL signs. The data set is challenging due to the great similarity among hand shapes and motions in the collected signs. Along with the data set, a sign language recognition algorithm is presented. The authors’ proposed method consists of three major stages: hand segmentation, hand shape sequence and body motion description, and sign classification. The hand shape segmentation is based on the depth and position of the hand joints. Histograms of oriented gradients and principal component analysis are applied on the segmented hand shapes to obtain the hand shape sequence descriptor. The covariance of the three-dimensional joints of the upper half of the skeleton in addition to the hand states and face properties are adopted for motion sequence description. The canonical correlation analysis and random forest classifiers are used for classification. The achieved accuracy is 55.57% over 150 ArSL signs, which is considered promising.

Head Pose Estimation on Top of Haar-Like Face Detection: A Study Using the Kinect Sensor

Abstract: Head pose estimation is a crucial initial task for human face analysis, which is employed in several computer vision systems, such as: facial expression recognition, head gesture recognition, yawn detection, etc. In this work, we propose a frame-based approach to estimate the head pose on top of the Viola and Jones (VJ) Haar-like face detector. Several appearance and depth-based feature types are employed for the pose estimation, where comparisons between them in terms of accuracy and speed are presented. It is clearly shown through this work that using the depth data, we improve the accuracy of the head pose estimation. Additionally, we can spot positive detections, faces in profile views detected by the frontal model, that are wrongly cropped due to background disturbances. We introduce a new depth-based feature descriptor that provides competitive estimation results with a lower computation time. Evaluation on a benchmark Kinect database shows that the histogram of oriented gradients and the developed depth-based features are more distinctive for the head pose estimation, where they compare favorably to the current state-of-the-art approaches. Using a concatenation of the aforementioned feature types, we achieved a head pose estimation with average errors not exceeding 5:1; 4:6; 4:2 for pitch, yaw and roll angles, respectively.

Recognition of basketball referee signals from real-time videos

Abstract: Recognition of hand gestures (hand signals) is an active research area for human computer interaction with many possible applications. Automatic machine vision-based hand gesture interfaces for real-time applications require fast and extremely robust human, pose and hand detection, and gesture recognition. Attempting to recognize gestures performed by official referees in sports (such as basketball game) video places tough requirements on the image segmentation techniques. Here we propose an image segmentation technique based on the histogram of oriented gradients and local binary pattern (LBP) features, which allow recognizing the signals of basketball referee from recorded game videos and achieved an accuracy of 95.6% using LBP features and support vector machine for classification. Our results are relevant for real-time analysis of basketball game.

Indian sign language recognition using ANN and SVM classifiers

Abstract: Sign language is an accepted language for communication between deaf and dumb community people It is the most significant way of communication between normal people and hearing and speech impaired people without the need of an interpreter Every country has its own developed Sign Language In India, this dialect is known as Indian Sign Language This research work aims at developing an automatic recognition system for Indian Sign Language numerals (0–9) The database used for implementation is self-created and consists of 1000 images, 100 images per numeral sign Shape descriptors, Scale Invariant Feature Transform (SIFT) and Histogram of Oriented Gradients (HOG) techniques are used for extracting desired features Artificial Neural Networks (ANN) and Support Vector Machine (SVM) classifiers are used to classify the signs This system achieves accuracy as high as 99%