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.

A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery

Abstract: Automatic oil tank detection plays a very important role for remote sensing image processing. To accomplish the task, a hierarchical oil tank detector with deep surrounding features is proposed in this paper. The surrounding features extracted by the deep learning model aim at making the oil tanks more easily to recognize, since the appearance of oil tanks is a circle and this information is not enough to separate targets from the complex background. The proposed method is divided into three modules: 1) candidate selection; 2) feature extraction; and 3) classification. First, a modified ellipse and line segment detector (ELSD) based on gradient orientation is used to select candidates in the image. Afterward, the feature combing local and surrounding information together is extracted to represent the target. Histogram of oriented gradients (HOG) which can reliably capture the shape information is extracted to characterize the local patch. For the surrounding area, the convolutional neural network (CNN) trained in ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012) contest is applied as a blackbox feature extractor to extract rich surrounding feature. Then, the linear support vector machine (SVM) is utilized as the classifier to give the final output. Experimental results indicate that the proposed method is robust under different complex backgrounds and has high detection rate with low false alarm.

Robust Ship Tracking via Multi-view Learning and Sparse Representation

Abstract: Conventional visual ship tracking methods employ single and shallow features for the ship tracking task, which may fail when a ship presents a different appearance and shape in maritime surveillance videos To overcome this difficulty, we propose to employ a multi-view learning algorithm to extract a highly coupled and robust ship descriptor from multiple distinct ship feature sets First, we explore multiple distinct ship feature sets consisting of a Laplacian-of-Gaussian (LoG) descriptor, a Local Binary Patterns (LBP) descriptor, a Gabor filter, a Histogram of Oriented Gradients (HOG) descriptor and a Canny descriptor, which present geometry structure, texture and contour information, and more Then, we propose a framework for integrating a multi-view learning algorithm and a sparse representation method to track ships efficiently and effectively Finally, our framework is evaluated in four typical maritime surveillance scenarios The experimental results show that the proposed framework outperforms the conventional and typical ship tracking methods

Automatic detection of bike-riders without helmet using surveillance videos in real-time

Abstract: In this paper, we propose an approach for automatic detection of bike-riders without helmet using surveillance videos in real time. The proposed approach first detects bike riders from surveillance video using background subtraction and object segmentation. Then it determines whether bike-rider is using a helmet or not using visual features and binary classifier. Also, we present a consolidation approach for violation reporting which helps in improving reliability of the proposed approach. In order to evaluate our approach, we have provided a performance comparison of three widely used feature representations namely histogram of oriented gradients (HOG), scale-invariant feature transform (SIFT), and local binary patterns (LBP) for classification. The experimental results show detection accuracy of 93.80% on the real world surveillance data. It has also been shown that proposed approach is computationally less expensive and performs in real-time with a processing time of 11.58 ms per frame.

Video classification with Densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off

Abstract: The current state-of-the-art in video classification is based on Bag-of-Words using local visual descriptors. Most commonly these are histogram of oriented gradients (HOG), histogram of optical flow (HOF) and motion boundary histograms (MBH) descriptors. While such approach is very powerful for classification, it is also computationally expensive. This paper addresses the problem of computational efficiency. Specifically: (1) We propose several speed-ups for densely sampled HOG, HOF and MBH descriptors and release Matlab code; (2) We investigate the trade-off between accuracy and computational efficiency of descriptors in terms of frame sampling rate and type of Optical Flow method; (3) We investigate the trade-off between accuracy and computational efficiency for computing the feature vocabulary, using and comparing most of the commonly adopted vector quantization techniques: \(k\)-means, hierarchical \(k\)-means, Random Forests, Fisher Vectors and VLAD.