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
TL;DR: A hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used, and tests are done to check the presence of pedestrians in the generated hypotheses.
Abstract: In this paper, we propose a hybrid system for pedestrian detection, in which both thermal and visible images of the same scene are used. The proposed method is achieved in two basic steps: (1) Hypotheses generation (HG) where the locations of possible pedestrians in an image are determined and (2) hypotheses verification (HV), where tests are done to check the presence of pedestrians in the generated hypotheses. HG step segments the thermal image using a modified version of OTSU thresholding technique. The segmentation results are mapped into the corresponding visible image to obtain the regions of interests (possible pedestrians). A post-processing is done on the resulting regions of interests to keep only significant ones. HV is performed using random forest as classifier and a color-based histogram of oriented gradients (HOG) together with the histograms of oriented optical flow (HOOF) as features. The proposed approach has been tested on OSU Color-Thermal, INO Video Analytics and LITIV data sets and the results justify its effectiveness.
33 citations
TL;DR: A sparse representation based approach is proposed for pedestrian detection from thermal images using the histogram of sparse code to represent image features and detecting pedestrian with the extracted features in an unimodal and a multimodal framework respectively.
33 citations
23 Jun 2008
TL;DR: A method for detecting people in low resolution infrared videos based on extracting gradient histograms from recursively generated patches and subsequently computing histogram ratios between the patches is presented.
Abstract: In this paper we present a method for detecting people in low resolution infrared videos. We further explore the feature set based on histogram of gradients beyond the well received HOG descriptors. Our approach is based on extracting gradient histograms from recursively generated patches and subsequently computing histogram ratios between the patches. Each set of patches is defined in terms of relative position within the search window, and each set is then recursively applied to extract smaller patches. The histogram of gradient ratios between patches become the feature vector. We adopted a linear SVM classifier as it provides a fast and effective framework for feature descriptor processing with minimal parameter tuning. Experimental results are presented on various OTCBVS datasets.
33 citations
13 Jun 2010
TL;DR: This work proposes a scheme for compressing distributions called Type Coding, which offers lower complexity and higher compression efficiency compared to tree-based quantization schemes proposed in prior work, and constructs optimal Entropy Constrained Vector Quantization (ECVQ) code-books and shows that Type Coded comes close to achieving optimal performance.
Abstract: We study different quantization schemes for the Compressed Histogram of Gradients (CHoG) image feature descriptor. We propose a scheme for compressing distributions called Type Coding, which offers lower complexity and higher compression efficiency compared to tree-based quantization schemes proposed in prior work. We construct optimal Entropy Constrained Vector Quantization (ECVQ) code-books and show that Type Coding comes close to achieving optimal performance. The proposed descriptors are 16× smaller than SIFT and perform on par. We implement the descriptor in a mobile image retrieval system and for a database of 1 million CD, DVD and book covers, we achieve 96% retrieval accuracy using only 4 kilobytes of data per query image.
33 citations
TL;DR: Simulation experiments show that PHOG has obvious advantages in the extraction of discriminating information from handwriting signature images compared with many previously proposed signature feature extraction approaches.
Abstract: Purpose – The purpose of this paper is to propose an effective method to perform off‐line signature verification and identification by applying a local shape descriptor pyramid histogram of oriented gradients (PHOGs), which represents local shape of an image by a histogram of edge orientations computed for each image sub‐region, quantized into a number of bins. Each bin in the PHOG histogram represents the number of edges that have orientations within a certain angular range.Design/methodology/approach – Automatic signature verification and identification are then studied in the general binary and multi‐class pattern classification framework, with five different common applied classifiers thoroughly compared.Findings – Simulation experiments show that PHOG has obvious advantages in the extraction of discriminating information from handwriting signature images compared with many previously proposed signature feature extraction approaches. The experiments also demonstrate that several classifiers, including...
33 citations