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.

High-speed object matching and localization using gradient orientation features

Abstract: In many robotics and automation applications, it is often required to detect a given object and determine its pose (position and orientation) from input images with high speed, high robustness to photometric changes, and high pose accuracy. We propose a new object matching method that improves efficiency over existing approaches by decomposing orientation and position estimation into two cascade steps. In the first step, an initial position and orientation is found by matching with Histogram of Oriented Gradients (HOG), reducing orientation search from 2D template matching to 1D correlation matching. In the second step, a more precise orientation and position is computed by matching based on Dominant Orientation Template (DOT), using robust edge orientation features. The cascade combination of the HOG and DOT feature for high-speed and robust object matching is the key novelty of the proposed method. Experimental evaluation was performed with real-world single-object and multi-object inspection datasets, using software implementations on an Atom CPU platform. Our results show that the proposed method achieves significant speed improvement compared to an already accelerated template matching method at comparable accuracy performance.

Human Detection for Night Surveillance using Adaptive Background Subtracted Image

Abstract: Surveillance based on Computer Vision has become a major necessity in current era. Most of the surveillance systems operate on visible light imaging, but performance based on visible light imaging is limited due to some factors like variation in light intensity during the daytime. The matter of concern lies in the need for processing images in low light, such as in the need of nighttime surveillance. In this paper, we have proposed a novel approach for human detection using FLIR(Forward Looking Infrared) camera. As the principle involves sensing based on thermal radiation in the Near IR Region, it is possible to detect Humans from an image captured using a FLIR camera even in low light. The proposed method for human detection involves processing of Thermal images by using HOG (Histogram of Oriented Gradients) feature extraction technique along with some enhancements. The principle of the proposed technique lies in an adaptive background subtraction algorithm, which works in association with the HOG technique. By means of this method, we are able to reduce execution time, precision and some other parameters, which result in improvement of overall accuracy of the human detection system.

A novel image matching algorithm based on sliding histograms of oriented gradients

Abstract: A novel algorithm for image matching based on recursive calculation of histograms of oriented gradients over several circular sliding windows and pyramidal image decomposition is presented. The algorithm gives good results for geometrically distorted and scaled scene images. The results of computer simulation obtained with the proposed algorithm are compared to those of available algorithms in terms of matching accuracy and processing time.

Improved Human Detection with a Fusion of Laser Scanner and Vision/Infrared Information for Mobile Applications

Abstract: This paper presents a method for human detection using a laser scanner with vision or infrared images. Mobile applications require reliable and efficient methods for human detection, especially as a part of driver assistance systems, including pedestrian collision systems. The authors propose an efficient method for multimodal human detection based on a combination of the features and context information. Strictly, the human is detected in the vision/infrared images using a combination of local binary patterns and histogram of oriented gradients features with a neural network in a cascade manner. Next, using coordinates of detected humans from the vision system, the moving trajectory is predicted until the scanner working distance is reached by the individual human. Then the segmentation of data from the laser scanner is further carried out with respect to the predicted trajectory. Finally, human detection in the laser scanner working distance is performed based on modelling of the human legs. The modelling is based on the adaptive breakpoint detection algorithm and proposed improved polylines definition and fitting algorithm. The authors conducted a set of experiments in predefined scenarios, discussed the identified weakness and advantages of the proposed method, and outlined detailed future work, especially for night-time and low-light conditions.

GOOFeD: Extracting Advanced Features for Image Classification via Improved Genetic Programming

Abstract: Feature extraction is widely considered one of the most critical components to classification performance in computer vision. In the past, human-designed features, such as the histogram of oriented gradients, were used for extracting statistically rich features. Recently, there has been a movement away from human-designed features to machine-learned features. Herein, we propose a novel genetic programming (GP) approach, coined GOOFeD, to automatically generate discriminative-rich features for image classification. This is achieved by greatly advancing GP in three ways: (1) promoting population diversity and redundancy removal, (2) introducing a unique adaptive mutation approach, and (3) controlling tree bloat through a new crossover technique. These improvements also lead to a population size required for learning that is smaller than that commonly used in the literature. To assess performance, GOOFeD is tested on the MIT urban and nature scene data set and a real-world buried explosive hazard data set. Experiments verify that, in terms of classification accuracy, GOOFeD outperforms many of the state-of-the-art human-designed features and feature learning techniques.