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.

Large scale sign detection using HOG feature variants

Abstract: In this paper we present two variant formulations of the well-known Histogram of Oriented Gradients (HOG) features and provide a comparison of these features on a large scale sign detection problem. The aim of this research is to find features capable of driving further improvements atop a preexisting detection framework used commercially to detect traffic signs on the scale of entire national road networks (1000's of kilometres of video). We assume the computationally efficient framework of a cascade of boosted weak classifiers. Rather than comparing features on the general problem of detection we compare their merits in the final stages of a cascaded detection problem where a feature's ability to reduce error is valued more highly than computational efficiency. Results show the benefit of the two new features on a New Zealand speed sign detection problem. We also note the importance of using non-sign training and validation instances taken from the same video data that contains the training and validation positives. This is attributed to the potential for the more powerful HOG features to overfit on specific local patterns which may be present in alternative video data.

Deep learning feature extraction for target recognition and classification in underwater sonar images

Abstract: This paper presents an automatic target recognition (ATR) approach for sonar onboard unmanned underwater vehicles (UUVs). In this approach, target features are extracted by a convolutional neural network (CNN) operating on sonar images, and then classified by a support vector machine (SMV) that is trained based on manually labeled data. The proposed approach is tested on a set of sonar images obtained by a UUV equipped with side-scan sonar. Automatic target recognition is achieved through the use of matched filters, while target classification is achieved with the trained SVM classifier based on features extracted by the CNN. The results show that deep learning feature extraction provide better performance compared to using other feature extraction techniques such as histogram of oriented gradients (HOG) and local binary pattern (LBP). By processing images autonomously, the proposed approach can be combined with onboard planning and control systems to develop autonomous UUVs able to search for underwater targets without human intervention.

Multiple Kernel Learning for vehicle detection in wide area motion imagery

Abstract: Vehicle detection in wide area motion imagery (WAMI) is an important problem in computer science, which if solved, supports urban traffic management, emergency responder routing, and accident discovery Due to large amount of camera motion, the small number of pixels on target objects, and the low frame rate of the WAMI data, vehicle detection is much more challenging than the task in traditional video imagery Since the object in wide area imagery covers a few pixels, feature information of shape, texture, and appearance information are limited for vehicle detection and classification performance Histogram of Gradients (HOG) and Haar descriptors have been used in human and face detection successfully, only using the intensity of an image, and HOG and Haar descriptors have different advantages In this paper, we propose a classification scheme which combines HOG and Haar descriptors by using Generalized Multiple Kernel Learning (GMKL) that can learn the trade-off between HOG and Haar descriptors by constructing an optimal kernel with many base kernels Due to the large number of Haar features, we first use a cascade of boosting classifier which is a variant of Gentle AdaBoost and has the ability to do feature selection to select a small number of features from a huge feature set Then, we combine the HOG descriptors and the selected Haar features and use GMKL to train the final classifier In our experiments, we evaluate the performance of HOG+Haar with GMKL, HOG with GMKL, Haar with GMKL, and also the cascaded boosting classifier on Columbus Large Image Format (CLIF) dataset Experimental results show that the fusion of the HOG+Haar with GMKL outperforms the other three classification schemes

HOG-ShipCLSNet: A Novel Deep Learning Network With HOG Feature Fusion for SAR Ship Classification

Abstract: Ship classification in synthetic aperture radar (SAR) images is a fundamental and significant step in ocean surveillance. Recently, with the rise of deep learning (DL), modern abstract features from convolutional neural networks (CNNs) have hugely improved SAR ship classification accuracy. However, most existing CNN-based SAR ship classifiers overly rely on abstract features, but uncritically abandon traditional mature hand-crafted features, which may incur some challenges for further improving accuracy. Hence, this article proposes a novel DL network with histogram of oriented gradient (HOG) feature fusion (HOG-ShipCLSNet) for preferable SAR ship classification. In HOG-ShipCLSNet, four mechanisms are proposed to ensure superior classification accuracy, that is, 1) a multiscale classification mechanism (MS-CLS-Mechanism); 2) a global self-attention mechanism (GS-ATT-Mechanism); 3) a fully connected balance mechanism (FC-BAL-Mechanism); and 4) an HOG feature fusion mechanism (HOG-FF-Mechanism). We perform sufficient ablation studies to confirm the effectiveness of these four mechanisms. Finally, our experimental results on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) jointly reveal that HOG-ShipCLSNet dramatically outperforms both modern CNN-based methods and traditional hand-crafted feature methods.