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.

A hybrid technique for gender classification with SLBP and HOG features

Abstract: Gender classification from facial images plays a significant role in biometric technology viz. gender medicine, surveillance, electronic banking system and human computer interaction. However, it has many challenges due to variations of pose, expression, aging, race, make-up, occlusion and illumination. In the proposed system, spatially enhanced local binary pattern (SLBP) and histogram of oriented gradients (HOG) are extracted to classify the human gender with SVM classifier. This hybrid feature selection has increased the power of the proposed system due to its representation of texture micro-patterns and local shape by capturing the edge or gradient structure form the image. The gender classification accuracy is studied by using the local feature representation of the face images separately and also these features are concatenated to provide a better recognition rate. The combination of two different local descriptors provides good representation of face image and this is given to SVM classifier which classifies as male or female. Also, the proposed work is compared with other two traditional classifiers such as k-nearest neighbor and sparse representation classifier. The performance was evaluated on FERET and LFW database. The highest classification accuracy 99.1% is achieved on FERET database and 95.7% is achieved on LFW database by applying cubic SVM with fusion of SLBP and HOG features.

HOG-like gradient-based descriptor for visual vehicle detection

Abstract: One of the main challenges for intelligent vehicles is the capability of detecting other vehicles in their environment, which constitute the main source of accidents. Specifically, many methods have been proposed in the literature for video-based vehicle detection. Most of them perform supervised classification using some appearance-related feature, in particular, symmetry has been extensively utilized. However, an in-depth analysis of the classification power of this feature is missing. As a first contribution of this paper, a thorough study of the classification performance of symmetry is presented within a Bayesian decision framework. This study reveals that the performance of symmetry-based classification is very limited. Therefore, as a second contribution, a new gradient-based descriptor is proposed for vehicle detection. This descriptor exploits the known rectangular structure of vehicle rears within a Histogram of Gradients (HOG)-based framework. Experiments show that the proposed descriptor outperforms largely symmetry as a feature for vehicle verification, achieving classification rates over 90%.

Human Action Recognition Based on DMMs, HOGs and Contourlet Transform

Abstract: This paper proposes a framework for recognizing human actions from depth video sequences by designing a novel feature descriptor based on Depth Motion Maps (DMMs), Contour let Transform (CT) and Histogram of Oriented Gradients (HOGs). First, CT is implemented on the generated DMMs of a depth video sequence and then HOGs are computed for each contour let sub-band. Finally, the concatenation of these HOG features is used as a feature descriptor for the depth video sequence. With this new feature descriptor, the l2-regularized collaborative representation classifier is utilized to recognize human actions. The experimental results on Microsoft Research Action3D dataset demonstrate that our proposed method can achieve the state-of-the-art performance for human activity recognition due to the precise feature extraction of contour let transform on the DMMs.

Action recognition based on binary patterns of action-history and histogram of oriented gradient

Abstract: In this paper, we have focused on the view-based spatio-temporal template matching approach for human action detection and classification. We have proposed an approach for human activity modeling that describes human motions as a texture pattern. We have combined two relatively simple feature extractors for obtaining a system to get more accurate result. In this method, video sequences are converted into temporal templates called Motion History Image (MHI), which can preserve dominant motion information. The local features are described with Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG) descriptors. LBP operator is texture operator that encodes the direction of motion from the non-monotonous areas of MHI images. HOG is used as feature descriptor and extracts the features from LBP. These descriptors are used to train with Support Vector Machine (SVM) classifier to recognize various action classes. This proposed method has been tested on the KTH Action Dataset (which is one of the most widely used benchmark datasets for human action classification), and on the Pedestrian Action Dataset. Our method has shown 86.67 % recognition rate in the 6-classes of KTH Action Dataset and 94.3 % accuracy in the 7-classes of Pedestrian Action Dataset. Based on the complexity of datasets, both the results are quite satisfactory.