An efficient HOG–ALBP feature for pedestrian detection

In this paper, we present a pedestrian detection method based on the combination of Histograms of Oriented Gradient (HOG) feature and uniform local binary pattern (LBP) feature, which can detect pedestrian accurately. To the problem of low recognition rate for a single feature, we combine contour information and texture information, and propose the cascade of the two types of features, HOG features and LBP features as the feature set. In order to compare the experimental results, Gentle AdaBoost is used to train the pedestrian classifier on the INRIA dataset. The experimental results show that these two features of pedestrian detection algorithm improve the accuracy and reduce the error rate. Our method achieve a detection rate of 94.05% at FPPW = 10 -4 , which is better than Dalal’s (detection rate of 84% to 89% at 10 -4 FPPW).

As artificial intelligence develops, deep learning algorithms are increasingly being used in the field of dorsal hand vein (DHV) recognition. However, deep learning has high requirements regarding the number of samples, and current DHV datasets have few images. To solve the above problems, we propose a method based on the fusion of ResNet and Histograms of Oriented Gradients (HOG) features, in which the shallow semantic information extracted by primary convolution and HOG features are fed into the residual structure of ResNet for full fusion and, finally, classification. By adding Gaussian noise, the North China University of Technology dataset, the Shandong University of Science and Technology dataset, and the Eastern Mediterranean University dataset are extended and fused to from a fused dataset. Our proposed method is applied to the above datasets, and the experimental results show that our proposed method achieves good recognition rates on each of the datasets. Importantly, we achieved a 93.47% recognition rate on the fused dataset, which was 2.31% and 26.08% higher than using ResNet and HOG alone.

Extraction of global face appearance and local interior differences is essential for any face recognition application. This paper presents a novel framework for face recognition by combining two effective descriptors namely, Zernike moments (ZM) and histogram of oriented gradients (HOG). ZMs are global descriptors that are invariant to image rotation, noise and scale. HOGs capture local details and are robust to illumination changes. Fusion of these two descriptors combines the merits of both local and global approaches and is effective against diverse variations present in face images. Further, as the processing time of HOG features is high owing to its large dimensionality, so, the study proposes to improve its performance by selecting only most discriminative HOG features (named discriminative HOG (DHOG)) for performing recognition. Efficacy of the proposed methods (DHOG, [Formula: see text] and [Formula: see text]) is tested on ORL, Yale and FERET databases. DHOG provides an improvement of 3% to 5% over the existing HOG approach. Recognition results achieved by [Formula: see text] and [Formula: see text] are up to 15% and 18% higher respectively than those obtained with these descriptors individually. Performance is also analyzed on LFW face database and compared with recent and state-of-the-art methods.

In recent years, pedestrian detection has attracted more attention in many practical applications. In this paper, a novel pedestrian detection method using quaternion histograms of oriented gradients (QHOG) was proposed, which is integrated the advantages of both the quaternionic representation and HOG feature of a color image. Firstly, the quaternionic representation was performed on the color image in the sliding window, and then the histograms of oriented gradients (HOG) feature was extracted over the quaternionic representation map. After that, the QHOG histogram was constructed to represent the sliding window. Two groups of experiments are performed on two popular pedestrian datasets, INRIA dataset and Daimler Chrysler (DC) dataset, respectively. The experimental results show that our proposed QHOG detector performs better than the HOG detector, HOG-LBP detector and MWLD detector.

This paper presents a popular method called boosted hog features to detect pedestrians and vehicles in static images. We compared the differences and similarities of detecting pedestrians and vehicles, then we selected boosted hog features to get an satisfying result. In the part of detecting pedestrians, Histograms of Oriented Gradients (HOG) feature is applied as the basic feature due to its good performance in various kinds of background. On that basis, we create a new feature with boosting algorithm to obtain more accurate results. In the part of detecting vehicle, we select the shadow underneath vehicle as the feature, so we can utilize it to detect vehicles in daytime. The shadow is an important feature for vehicles in traffic scenes. The region under vehicle is usually darker than other objects or backgrounds and can be segmented by setting a threshold. Finally, experimental results show that the detection of pedestrians and vehicles using boosted hog feature and linear svm combines the advantages of hog feature and adaboost classifier, and can achieve better detection results than the detector using conditional HOG features. At the end, the paper shows its efficiency and effectiveness using to application in real situations.

Face recognition is efficiently used in computer vision, pattern recognition, biometric, law enforcement, surveillance, criminal investigations, and missing person detection. However, face recognition is still a challenging task under variant illuminations, pose, expression, occlusion, and background. In this paper, we proposed a cascading of histogram of oriented gradients (HOG) and local binary pattern (LBP) feature extraction method on ORL face dataset, which can improve recognition rates while also addressing pose, scale, expression, and variant illumination issues. The facial recognition network was trained using various classifiers such as with KNN, SVM, and random forest (RF). The results have been shown that the accuracy rate of the combined LBP and HOG feature extraction method is better than individual LBP or HOG features for ORL database face recognition.

The estimation of the number of people in surveillance areas is essential for monitoring crowded scenes. When density of a zone increases to a certain approximated level, people's safety can be endangered. Detection of human is a prerequisite for density estimation, tracking, activity recognition and anomaly detection even in non congested areas. This paper presents a robust hybrid approach for face detection in crowd by combining the skin color segmentation and a Histogram of Oriented Gradients(HOG) with Support Vector Machine(SVM) architecture. Initially, image enhancement is performed to improve the detection rate. An edge preserving pyramidal approach is applied for multiscale representation of an image. Skin color segmentation is done with combination of YCbCr and RGB color model, and HOG features are extracted from the segmented skin region. We trained the SVM classifier by Muct and FEI databases which consist 751 and 2800 face images respectively. The accuracy of this approach is evaluated by testing it on BAO multiple face database and on various manually collected images captured in surveillance areas. Experimental results demonstrate that the supplementary skin color segmentation with HOG is more potent for increasing the detection rate than using HOG features only. The proposed approach achieves 98.02% accuracy which is higher in comparison to Viola Jones and fast face detection method.

In order to detect pedestrians in many applications, histogram of oriented gradient (HOG) feature is widely used. Although HOG feature can provide high detection accuracy, fast detection time is hardly achieved due to its computational complexity. In this paper, a new method for pedestrian detection using HOG feature is proposed to achieve both high detection rate and fast detection time. Multi-scale cells and blocks of HOG feature are used to improve detection accuracy, and they are computed by using integral HOG to improve detection speed. In order to further improve detection quality in terms of accuracy and speed, two-stage approach is adopted in the proposed method. Experimental results show that the proposed method improves both detection rate and speed.

Detecting pedestrians accurately is the first fundamental step for many computer vision applications such as video surveillance, smart vehicles, intersection traffic analysis and so on. The authors present an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on the DaimlerChrysler benchmarking data set, the MIT CBCL data set and ’Intitut National de Recherche en Informatique et Automatique (INRIA) data set. All can be publicly accessed. The experimental results show that region covariance features with radial basis function kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM. Furthermore, the results reveal that both covariance and HOG features perform very well in the context of pedestrian detection.

The ability to detect pedestrians is a first important step in many computer vision applications such as video surveillance. This paper presents an experimental study on pedestrian detection using state-of-the-art local feature extraction and support vector machine (SVM) classifiers. The performance of pedestrian detection using region covariance, histogram of oriented gradients (HOG) and local receptive fields (LRF) feature descriptors is experimentally evaluated. The experiments are performed on both the benchmarking dataset used in [1] and the MIT CBCL dataset. Both can be publicly accessed. The experimental results show that region covariance features with radial basis function (RBF) kernel SVM and HOG features with quadratic kernel SVM outperform the combination of LRF features with quadratic kernel SVM reported in [1].

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.

Pedestrian detection is one of the challenging research topics in computer vision and efficient feature representation of a pedestrian attracts more and more attention. Traditional features such as Histogram of Oriented Gradients (HOG) were widely used in pedestrian detection, but because of their poor texture description ability, these feature based methods cannot achieve satisfactory pedestrian detection results. In this paper, the Local Ternary Patterns (LTP) is introduced into pedestrian detection, and a new feature, named Sqrt-LTP feature, is proposed by finding the square root of LTP feature. Experimental results on INRIA database set show that our Sqrt-LTP feature based pedestrian detection method can achieve more robust performance than both traditional HOG and LTP based approaches, indicating that the proposed Sqrt-LTP feature is a more discriminative feature and can be used for texture classification.

Feature extraction plays a pivotal role in pattern recognition and matching. An ideal feature should be invariant to image transformations such as translation, rotation, scaling, etc. In this work, we present a novel rotation-invariant feature, which is based on Histogram of Oriented Gradients (HOG). We compare performance of the proposed approach with the HOG feature on 2D phantom data, as well as 3D medical imaging data. We have used traditional histogram comparison measures such as Bhattacharyya distance and Normalized Correlation Coefficient (NCC) to assess efficacy of the proposed approach under effects of image rotation. In our experiments, the proposed feature performs 40%, 20%, and 28% better than the HOG feature on phantom (2D), Computed Tomography (CT-3D), and Ultrasound (US-3D) data for image matching, and landmark tracking tasks respectively.

Histogram of Oriented Gradients (HOG) features are the underlying representation in automotive computer vision applications such as collision avoidance and lane keeping. In these applications, we have observed that HOG feature computation is often a slow and energy-intensive component of the overall pipeline. In this paper, we focus on reducing both the time taken and the energy used for computing Felzenszwalb HOG features. We achieve our results though a combination of reduced precision, SIMD parallelism, algorithmic changes, and outer-loop parallelism. In particular, we address a bottleneck in histogram accumulation by phrasing the problem as a gather instead of the (traditional) scatter. Additionally, we explore the tradeoffs of using L1 instead of L2 norms to compute gradients, which enables smaller operands and more SIMD parallelism. Overall, we are able to compute multiresolution HOG pyramids at 70fps for 640×480 images on a multicore CPU. This is a 3.6x speedup over the best known HOG implementation and a 29× speedup over the popular voc-release5 HOG code. This is also a 3.6× - 22× reduction in energy per frame compared to previous HOG implementations. Our open-source implementation is available for download.

Histogram of Oriented Gradients (HOG) feature descriptor is very effective to represent objects and is widely used in human and face detection. In this paper, HOG feature descriptor is applied for Content-based Image Retrieval (CBIR). For handling similarity measurement of large amount of database, vocabulary tree is used. Experimental results illustrate the comparative analysis of retrieval system based on HOG feature descriptor and Gabor transform feature descriptor. It is verified that HOG-based retrieval system improves Average Precision (AP) and Average Recall (AR) (56.75% and 38.45%, respectively) from Gabor-transform-based retrieval system (41.20% and 25.41%, respectively). All the experiments are performed on Corel 1000 natural image database.