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.

Collaborative Learning based on Convolutional Features and Correlation Filter for Visual Tracking

Abstract: One of the most important and challenging research topics in the area of computer vision is visual object tracking, which is relevant to many real-world applications. Recently, discriminative correlation filters (DCF) have been demonstrated to overcome the problems in visual object tracking efficiently. So far, only single-resolution feature maps have been utilized in DCF. Owing to this limitation, the potential of DCF has not been exploited. Moreover, convolutional features have demonstrated a better performance for visual tracking than histogram of oriented gradients (HOG) features and color features. Based on these facts, in this paper, we propose collaborative learning based on multi-resolution feature maps for DCF, employing convolutional features. Further, the confidence score, which represents the location of the target object, is selected from various candidates based on certain rules. In addition, the continuous filters are trained to handle the variations of appearance of the target. The extensive experimental results obtained using VOT2015 and OTB-100 benchmark datasets show that the proposed algorithm performs favorably against state-of-the-art tracking algorithms.

Embedded vision system for pedestrian detection based on HOG+SVM and use of motion information implemented in Zynq heterogeneous device

Abstract: Pedestrian detection is a very important application of embedded real-time vision systems. It is essential in Advanced Driver Assistance Systems (ADAS) and Advanced Video Surveillance Systems (AVSS). The most widely used method involves a combination of Histogram of Oriented Gradients (HOG) features and Support Vector Machine (SVM) classifier. It offers quite high detection accuracy at the cost of high computational complexity. Therefore, it is impossible to use GPP-based (General Purpose Processor) embedded systems for this application. In this paper a hardware-software HOG+SVM pedestrian detection system implemented in a heterogeneous Xilinx Zynq device is presented. The base algorithm is supported with detection grouping and tracking procedures. They increase the performance of the solution, especially in case of temporal partially occluded or vague visible objects. The system is able to process a 1280 × 720 @ 60 fps video stream in real-time.

Identification and Classification of Mechanical Damage During Continuous Harvesting of Root Crops Using Computer Vision Methods

Abstract: Detecting sugar beetroot crops with mechanical damage using machine learning methods is necessary for fine-tuning beet harvester units. The Agrifac HEXX TRAXX harvester with an installed computer vision system was investigated. A video camera (24 fps) was installed above the turbine, which receives the dug-out beets after the digger and is connected to a single-board computer. At the preprocessing stage, static and insignificant image details were revealed. Canny edge detector and excess green minus excess red (ExGR) method were used. The identified areas were excluded from the image. The remaining areas were glued with similar areas of another image. As a result, the number of images entering the second stage of preprocessing was reduced by half. Then Otsu’s binarization was used. The main stage of image processing is divided into two sub-stages: detection and classification. The improved YOLOv4-tiny method was chosen for root crop detection using a single-board computer (SBC). This method allows processing up to 14 images of 416 $\times416$ pixels with 86% precision and 91% recall. To classify root crop damage, we considered two algorithms as candidates: 1. bag of visual words (BoVW) with a support vector machine (SVM) classifier using histogram of oriented gradients (HOG) and scale-invariant feature transform (SIFT) descriptors; 2. convolutional neural networks (CNN). Under normal lighting conditions, CNN showed the best accuracy, which was 99%. The implemented methods were used to detect and classify blurred images of sugar beetroots, which were previously rejected. For improved YOLOv4-tiny precision was 74% and recall was 70%. CNN classification accuracy was 92.6%.

Robust Face Identification with Small Sample Sizes using Bag of Words and Histogram of Oriented Gradients

Abstract: Face identification under small sample conditions is currently an active research area. In a case of very few reference samples, optimally exploiting the training data to make a model which has a low generalization error is an important challenge to create a robust face identification algorithm. In this paper we propose to combine the histogram of oriented gradients (HOG) and the bag of words (BOW) approach to use few training examples for robust face identification. In this HOG-BOW method, from every image many sub-images are first randomly cropped and given to the HOG feature extractor to compute many different feature vectors. Then these feature vectors are given to a K-means clustering algorithm to compute the centroids which serve as a codebook. This codebook is used by a sliding window to compute feature vectors for all training and test images. Finally, the feature vectors are fed into an L2 support vector machine to learn a linear model that will classify the test images. To show the efficiency of our method, we also experimented with two other feature extraction algorithms: HOG and the scale invariant feature transform (SIFT). All methods are compared on two well-known face image datasets with one to three training examples per person. The experimental results show that the HOG-BOW algorithm clearly outperforms the other methods.

Hybrid method for hand gesture recognition based on combination of Haar-like and HOG features

Abstract: In this paper a new method is proposed for hand gesture recognition The proposed method increases hand gesture recognition rate and decreases false positive error rate by using combination of Haar-like and Histogram of Oriented Gradients (HOG) features Also some new Haar-like features are proposed proportional to hand posture to solve major Haar-like problem that is high false positive error rate in hand posture recognition These features improve recognition rate to 83% The experiments showed that hybrid method can recognize hand gesture by 935% accuracy which is 25% higher than previous method, and decrease the false positive error from 92% to 8%