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.

Robust and real-time traffic light recognition based on hierarchical vision architecture

Abstract: ‘Circle’ and ‘arrow’ traffic lights are both common at intersections in urban road environment. However, existing purely vision based systems are only focus on either ‘circle’ or ‘arrow’ traffic light recognition, which limits their real-world application. In this paper, A novel robust and real-time traffic light recognition system based on hierarchical vision architecture is carefully designed. The system first learns a joint color space filter in normalized RGB and HSI color space to automatically select the traffic light candidate regions with connected component analysis, then detects traffic lights in the image neglecting the direction information with Multi-layer Histogram of Oriented Gradients (MHOG) feature, at last, it determines the direction information with the traditional Histogram of Oriented Gradients (HOG) feature extracted from the light-emitting unit and linear SVM classifiers. Experimental results show that the whole system can provide high recognition rate, and it can provide robust and real-time decision support for the intelligent vehicle.

Arabic Sign Language Alphabet Recognition Based on HOG-PCA Using Microsoft Kinect in Complex Backgrounds

Abstract: A complex background is a well-known problem in any vision-based sign language recognition system. This paper presents Arabic sign language alphabet recognition system in complex backgrounds using Microsoft Kinect. The proposed system passes by three phases. The first phase which is the signer segmentation process, focuses on the isolation of the active signer rapidly from the background. The system assumes that the active signer is the closest person to the Kinect sensor, so that the system isolates this person from any other persons or any skin-like object that may exist in the scene. After that, hand segmentation is achieved using RGB-Ratio color model. Histogram of Oriented Gradients (HOG) is extracted from the image then Principal Component Analysis (PCA) is applied on HOG so that HOG-PCA is used to train a support vector machine (SVM) classifier. The system is able to recognize the 30 Arabic alphabets with an accuracy of 99.2%. The proposed online system has the ability to recognize Arabic alphabet almost correctly in a reasonable response time.

Lean histogram of oriented gradients features for effective eye detection

Abstract: Reliable object detection is very important in computer vision and robotics applications. The histogram of oriented gradients (HOG) is established as one of the most popular hand-crafted features, which along with support vector machine (SVM) classification provides excellent performance for object recognition. We investigate dimensionality deduction on HOG features in combination with SVM classifiers to obtain efficient feature representation and improved classification performance. In addition to lean HOG features, we explore descriptors resulting from dimensionality reduction on histograms of binary descriptors. We consider three-dimensionality reduction techniques: standard principal component analysis, random projections, a computationally efficient linear mapping that is data independent, and locality preserving projections (LPP), which learns the manifold structure of the data. Our methods focus on the application of eye detection and were tested on an eye database created using the BioID and FERET face databases. Our results indicate that manifold learning is beneficial to classification utilizing HOG features. To demonstrate the broader usefulness of lean HOG features for object class recognition, we evaluated our system’s classification performance on the CalTech-101 dataset with favorable outcomes.