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.

Brazilian License Plate Detection Using Histogram of Oriented Gradients and Sliding Windows

Abstract: Due to the increasingly need for automatic traffic monitoring, vehicle license plate detection is of high interest to perform automatic toll collection, traffic law enforcement, parking lot access control, among others. In this paper, a sliding window approach based on Histogram of Oriented Gradients (HOG) features is used for Brazilian license plate detection. This approach consists in scanning the whole image in a multiscale fashion such that the license plate is located precisely. The main contribution of this work consists in a deep study of the best setup for HOG descriptors on the detection of Brazilian license plates, in which HOG have never been applied before. We also demonstrate the reliability of this method ensured by a recall higher than 98% (with a precision higher than 78%) in a publicly available data set.

A Local Feature Descriptor Based on Combination of Structure and Texture Information for Multispectral Image Matching

Abstract: Due to the significant nonlinear intensity changes of multispectral images, automatic image feature point matching is a challenging task. This letter addresses the problem and proposes a novel descriptor combining the structure and texture information to solve the nonlinear intensity variations of multispectral images. We first propose directional maps, i.e., the directional response maps (DMs) and the directional response binary maps (DBMs), which can capture the common structure and texture properties of multispectral images, respectively. We then use the spatial pooling pattern of the histogram of oriented gradients to separately describe the local region of each point of interest based on the DMs and DBMs. In order to speed up the calculation, we apply Gaussian filters to the DMs and average filters to the DBMs to construct the per-pixel histogram bins. Finally, we conjoin the normalized feature vectors corresponding to the structure description and texture description of each point of interest to obtain the histograms of directional maps (HoDMs). The proposed HoDM descriptor was evaluated using three data sets composed of images obtained in both visible light and infrared spectra. The experimental results confirm that the proposed HoDM descriptor is robust to the nonlinear intensity changes of multispectral images and has a superior matching performance as well as a much higher computational efficiency.

Fast traffic sign recognition using color segmentation and deep convolutional networks

Abstract: The use of Computer Vision techniques for the automatic recognition of road signs is fundamental for the development of intelligent vehicles and advanced driver assistance systems. In this paper, we describe a procedure based on color segmentation, Histogram of Oriented Gradients (HOG), and Convolutional Neural Networks (CNN) for detecting and classifying road signs. Detection is speeded up by a preprocessing step to reduce the search space, while classification is carried out by using a Deep Learning technique. A quantitative evaluation of the proposed approach has been conducted on the well-known German Traffic Sign data set and on the novel Data set of Italian Traffic Signs (DITS), which is publicly available and contains challenging sequences captured in adverse weather conditions and in an urban scenario at night-time. Experimental results demonstrate the effectiveness of the proposed approach in terms of both classification accuracy and computational speed.

Investigation of acoustic and visual features for acoustic scene classification

Abstract: Acoustic scene classification has gained great interests in recent years due to its diverse applications. Various acoustic and visual features have been proposed and evaluated. However, few studies have investigated acoustic and visual feature aggregation for acoustic scene classification. In this paper, we investigated various feature sets based on the fusion of acoustic and visual features. Specifically, acoustic features are directly extracted from the waveform: spectral centroid, spectral entropy, spectral flux, spectral roll-off, short-time energy, zero-crossing rate, and Mel-frequency Cepstral coefficients. For visual features, we calculate local binary pattern, histogram of gradients, and moments based on the audio scene time-frequency representation. Then, three feature selection algorithms are applied to various feature sets to reduce feature dimensionality: correlation-based feature selection, principal component analysis, and ReliefF. Experimental results show that our proposed system was able to achieve an accuracy improvement of 15.43% compared to the baseline system with the development set. When all development sets are used for training, the performance based on the evaluation set provided by the TUT Acoustic scene 2016 challenge is 87.44%, which is the fourth best among all non-neural network systems.

Fast hog feature computation based on CUDA

Abstract: Histogram of oriented gradients (HOG) is one of the most popular descriptors used for pedestrian detection, but this descriptor has its own drawback. Like most sliding window algorithms it is very slow, making it unsuitable for many real-time applications. This paper proposes a parallel implementation of the HOG algorithm. It bases on CUDA (compute unified device architecture) platform that could use parallel computing of graphic processing unit (GPU). The time consumption of HOG running on the GPU and on the CPU is compared by experiments in this paper. The results demonstrate that the HOG on GPU performs better than the HOG running on CPU, and is approximate 10 times speedup.