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.

Hardware architecture and optimization of sliding window based pedestrian detection on FPGA for high resolution images by varying local features

Abstract: Pedestrian detection has lately attracted considerable interest from researchers due to many practical applications. However, the low accuracy and high complexity of pedestrian detection has still not enabled its use in successful commercial applications. In this paper, we present insights into the complexity-accuracy relationship of pedestrian detection. We consider the Histogram of Oriented Gradients (HOG) scheme with linear Support Vector Machine (LinSVM) as a benchmark. We describe parallel implementations of various blocks of the pedestrian detection system which are designed for full-HD (1920×1080) resolution. Features are improved by optimal selection of cell size and histogram bins which have been shown to significantly affect the accuracy and complexity of pedestrian detection. It is seen that with a careful choice of these parameters a frame rate of 39.2 fps is achieved with a negligible loss in accuracy which is 16.3x and 3.8x higher than state of the art GPU and FPGA implementations respectively. Moreover 97.14% and 10.2% reduction in energy consumption is observed to process one frame. Finally, features are further enhanced by removing petty gradients in histograms which result in loss of accuracy. This increases the frame rate to 42.7 fps (18x and 4.1x higher) and lowers the energy consumption by 97.34% and 16.4% while improving the accuracy by 2% as compared to state of the art GPU and FPGA implementations respectively.

Pedestrian detection using improved histogram of oriented gradients

Abstract: Histogram of oriented gradients (HOG) has been proved to be an effective way of solving pedestrian detection in real scenes, but great computation amount makes it far from practical application. So we propose a new detection system based on improved HOG features with faster detecting speed. While calculating HOG, the pixels irrelevant to object contour shape are neglected. Then we decrease dimension number of the feature vector by 80%, making the SVM classification model much more simplified. Although the accuracy only drops down no more than 5%, our improved method performs over 5 times faster than the previous one. After entirely scanning a image, we use mean shift clustering algorithm to merge multiple positive responses belonging to same one positive case. Experiments show our method's performance on both test dataset and in real scene images.

Defect Recognition Method Based on HOG and SVM for Drone Inspection Images of Power Transmission Line

Abstract: This paper introduces the method for defect recognition of power transmission lines based on Histogram of Oriented Gradients (HOG) algorithm and Support Vector Machine (SVM) algorithm. Firstly, this paper investigates the key technologies in the system including image preprocessing, feature extraction methods, feature dimension reduction and classifiers. Secondly, according to the characteristics of power transmission line images, HOG is used to extract image features. HOG is a dense descriptor for the local overlapping area of the image. It constructs the feature by calculating the gradient direction histogram of the local region. Principal Component Analysis (PCA) method is applied to solve the feature dimension explosion. It can be used to extract the main feature components of the data and often used for dimensionality reduction of high-dimensional data. Thirdly, the SVM algorithm is used for classification. In the field of machine learning, it is a supervised learning model, which is usually used for pattern recognition, classification and regression analysis. The Directed Acyclic Graph (DAG) multi-classifiers is designed for the defect recognition of power transmission lines, such as normal lines, strand breakage lines and foreign-matter lines. In addition, the experimental results show that when the size of the pixel cell is 32*32 and the PCA contribution rate is 99%, the image processing has the best defect recognition performance, the processing speed of each image is 0.539 seconds and the average recognition accuracy is 84.3%.

High Performance Facial Expression Recognition System Using Facial Region Segmentation, Fusion of HOG & LBP Features and Multiclass SVM

Abstract: Facial expression is an effective method of nonverbal communication and expressing feelings. A method to recognize emotions through facial expressions is proposed in this paper. After some preprocessing of the input image, the facial region is segmented into four expression regions according to the highly effective proposed segmentation method. Features from these segmented parts are extracted using a fusion of Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP). Reduction of the dimension of the feature vector is done using Principal Component Analysis (PCA). For classifying the features and thus the expression images, multiclass Support Vector Machine (SVM) is used. The performance of the proposed method is measured using three publicly available and highly used datasets (JAFFE, CK+, RaFD). Finally, achieved performance is compared with performance on these datasets by other available methods to indicate that the proposed method succeeds in achieving state-of-the-art performance.