Topic
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.
Papers published on a yearly basis
Papers
More filters
••
01 Dec 2019TL;DR: An attendance system using face recognition, which is the best foot forward to reduce the loopholes encountered by these systems is presented and it was found that dlib using Convolutional Neural Networks (CNN) yielded better results than d Lib using Histogram Oriented Gradients (HOG).
Abstract: The importance of a properly maintained attendance system is very high. Although many systems are already existing, there are many loopholes present. This paper presents an attendance system using face recognition, which is the best foot forward to reduce the loopholes encountered by these systems. The Attendance system using face recognition consists of two phases, face detection and face recognition. The performance of detection algorithms such as Viola Jones and deep learning-based detection was compared and deep learning-based detection was preferred. On the recognition front, deep learning-based face recognition was used and optimum results were obtained. It was found that dlib using Convolutional Neural Networks (CNN) yielded better results than dlib using Histogram Oriented Gradients (HOG) because HOG only detects a frontal image, while CNN detects faces from all angles and ensures no discrepancies during face detection and recognition. Also, it was observed that the speed of training datasets was higher in CNN as it uses GPU as compared to HOG, which uses CPU for training the dataset as well as recognition.
8 citations
••
TL;DR: In this article, the authors presented a novel approach for image retrieval based on extraction of low level features using techniques such as Directional Binary Code, Haar Wavelet transform and Histogram of Oriented Gradients.
Abstract: In this paper, we present a novel approach for image retrieval based on extraction of low level features using techniques such as Directional Binary Code, Haar Wavelet transform and Histogram of Oriented Gradients. The DBC texture descriptor captures the spatial relationship between any pair of neighbourhood pixels in a local region along a given direction, while Local Binary Patterns descriptor considers the relationship between a given pixel and its surrounding neighbours. Therefore, DBC captures more spatial information than LBP and its variants, also it can extract more edge information than LBP. Hence, we employ DBC technique in order to extract grey level texture feature from each RGB channels individually and computed texture maps are further combined which represents colour texture features of an image. Then, we decomposed the extracted colour texture map and original image using Haar wavelet transform. Finally, we encode the shape and local features of wavelet transformed images using Histogram of Oriented Gradients for content based image retrieval. The performance of proposed method is compared with existing methods on two databases such as Wang's corel image and Caltech 256. The evaluation results show that our approach outperforms the existing methods for image retrieval.
8 citations
••
26 May 2014
TL;DR: A robust vehicle detection method for identifying horizontal edges by using a Sobel filter to achieve low computational complexity and can efficiently and effectively detect forward vehicle in various scenes is proposed.
Abstract: A video-based vehicle detection method must be capable of continual operation under various environmental and illumination conditions. Therefore, identifying features that can adapt to various conditions is crucial. This paper proposes a robust vehicle detection method for identifying horizontal edges by using a Sobel filter to achieve low computational complexity. Based on the orientation of the gradient, the feature of a vehicle can be extracted accurately and quickly. Additionally, symmetrical features are critical features, and a histogram of oriented gradients was employed to reduce the detection error rate. The experimental results indicate that the proposed method can efficiently and effectively detect forward vehicle in various scenes.
8 citations
••
03 Apr 2014TL;DR: The main contribution of this paper is to reduce the computational time of HOG by dynamically determining the region of interest and limiting the scan area and not only reduces detection time but also reduces the number of false positives and increases the efficiency.
Abstract: Human detection and tracking is an interesting field of research in computer vision and image processing areas. It is widely used in video surveillance, robotics, human machine interaction and other applications. The automated object detection and tracking is still a challenging task that needs to be addressed. Hence the main idea is to develop a system based on various image processing techniques to reliably detect and track people in video sequence from stationary cameras. The proposed method can be viewed as consisting of two stages namely detection and tracking. In detection stage, Histogram of Oriented Gradients popularly known as HOG is used as a feature descriptor. HOG features are robust to local changes in geometry and illumination but it is computationally expensive. This disadvantage of HOG is due to its exhaustive scanning approach over entire region of interest. The main contribution of this paper is to reduce the computational time of HOG by dynamically determining the region of interest and limiting the scan area. Further Principal component analysis is used to reduce the dimensionality of HOG features. The additional use of optical flow based tracking eliminates the need of HOG computation in every frame. Hence the person detected by HOG in initial frame is successfully tracked in subsequent frames and reduces HOG computation time. The proposed dynamic ROI selection method not only reduces detection time but also reduces the number of false positives and increases the efficiency. The experimental results show that the system efficiently detects and tracks people in videos without much of occlusion.
8 citations
••
TL;DR: The investigated detection algorithm introduces Histogram of Oriented Gradients and image entropy, whereas the tracking part employs Hough transform, Gabor filter and Kanade-Lucas-Tomasi optical flow estimation technique.
8 citations