Relevant bibliographies by topics / Histogram of Oriented Gradients (HOG)

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Histogram of Oriented Gradients (HOG)'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Histogram of Oriented Gradients (HOG).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Histogram of Oriented Gradients (HOG)"

1

Li, Bin, Kaili Cheng, and Zhezhou Yu. "Histogram of Oriented Gradient Based Gist Feature for Building Recognition." Computational Intelligence and Neuroscience 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/6749325.

Full text
Abstract:
We proposed a new method of gist feature extraction for building recognition and named the feature extracted by this method as the histogram of oriented gradient based gist (HOG-gist). The proposed method individually computes the normalized histograms of multiorientation gradients for the same image with four different scales. The traditional approach uses the Gabor filters with four angles and four different scales to extract orientation gist feature vectors from an image. Our method, in contrast, uses the normalized histogram of oriented gradient as orientation gist feature vectors of the same image. These HOG-based orientation gist vectors, combined with intensity and color gist feature vectors, are the proposed HOG-gist vectors. In general, the HOG-gist contains four multiorientation histograms (four orientation gist feature vectors), and its texture description ability is stronger than that of the traditional gist using Gabor filters with four angles. Experimental results using Sheffield Buildings Database verify the feasibility and effectiveness of the proposed HOG-gist.
APA, Harvard, Vancouver, ISO, and other styles
2

Guo, Lie, Guang Xi Zhang, Ping Shu Ge, and Lin Hui Li. "Pedestrian Tracking with HOG and Color Histogram Features." Applied Mechanics and Materials 241-244 (December 2012): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.498.

Full text
Abstract:
To improve the effectiveness of pedestrian tracking, the histograms of oriented gradients (HOG) and color histogram characteristics are adopted to track pedestrian based on particle filter. Firstly, the pedestrian is detected using the HOG features to determine the initial target position. Then the target is tracked based on particle filter utilizing color histogram, during which the HOG is used to modify particle heavy weights and particle sampling. Experimental results verify the accurateness and efficiency of the proposed method.
APA, Harvard, Vancouver, ISO, and other styles
3

Anggraeny, Fetty Tri, Basuki Rahmat, and Singgih Putra Pratama. "Deteksi Ikan Dengan Menggunakan Algoritma Histogram of Oriented Gradients." Informatika Mulawarman : Jurnal Ilmiah Ilmu Komputer 15, no. 2 (September 10, 2020): 114. http://dx.doi.org/10.30872/jim.v15i2.4648.

Full text
Abstract:
Indonesia merupakan negara yang kaya akan sumber daya alam baik hayati maupun non-hayati. Salah satu sumber daya alam hayati yang sangat banyak jumlahnya di Indonesia adalah laut, Untuk mempermudah mengidentifikasikan ikan, dapat memanfaatkan sebuah teknologi yang dapat membantu manusia untuk dapat mengenali ikan dengan menggunakan visi komputer dan pendekatan pemrosesan gambar untuk deteksi ikan dan bukan ikan menggunakan algoritma Histogram of Oriented Gradients (HOG) dan AdaBoost-SVM. Hasil penelitian menunjukkan bahwa metode HOG dan AdaBoost-SVM dapat menghasilkan tingkat akurasi rata-rata sebesar 84.8%.
APA, Harvard, Vancouver, ISO, and other styles
4

Zhang, Li Hong, and Lin Li. "Improved Pedestrian Detection Based on Extended Histogram of Oriented Gradients." Applied Mechanics and Materials 347-350 (August 2013): 3815–20. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3815.

Full text
Abstract:
In order to further improve pedestrian detection accuracy and avoid the disadvantage of original histogram of oriented gradients (HOG), differential template, overlap ratio and normalization method and so on are improved when HOG features are extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Multi-scale detection technique and non maxima suppression method are employed for precisely locating the pedestrians in the image. Experiments show that the human detection system improves detection accuracy and still maintains a relatively satisfactory speed.
APA, Harvard, Vancouver, ISO, and other styles
5

Zhang, Li Hong. "Human Detection Based on SVM and Improved Histogram of Oriented Gradients." Applied Mechanics and Materials 380-384 (August 2013): 3862–65. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3862.

Full text
Abstract:
Considering the fact that original histogram of oriented gradients (HOG) cannot extract the body local features in large image regions, its features are improved when extracted, then more gradient information are extracted and feature description operators can be obtained which describe human detail features better in lager image regions or detection windows. Considering speed, we select support vector machine (SVM) using linear function kernel as a classifier. Combining with HOG extraction and SVM training, the process includes three steps: features extraction, training and detection. Experiments show that while maintaining a relatively satisfactory speed the human detection system improves detection accuracy.
APA, Harvard, Vancouver, ISO, and other styles
6

El-Sayed, Rania Salah, and Mohamed Nour El-Sayed. "Classification of vehicles’ types using histogram oriented gradients: comparative study and modification." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 4 (December 1, 2020): 700. http://dx.doi.org/10.11591/ijai.v9.i4.pp700-712.

Full text
Abstract:
This paper proposes an efficient model for recognizing and classifying a vehicle type. The model localizes each object in the image then identifies the vehicle type. The features of an image are extracted using the histogram oriented gradients (HOG) and ant colony optimization (ACO). A vehicle type is determined using different classifiers namely: the k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and Softmax classifiers. The model is implemented and operated on two datasets of vehicles' images as test-beds. From the comparative study, the SVM outperforms the other adopted classifiers and is also better using HOG than that using ACO. A modification is done on HOG by adding the Laplacian filter to select the most significant image features. The accuracy of the SVM classifier using modified HOG outperforms that one using the traditional HOG. The proposed model is analyzed and discussed regardless the local geometric and photometric transformations like illumination variations.
APA, Harvard, Vancouver, ISO, and other styles
7

Hamada, Nuha H., and Faten F. Kharbat. "p-norms of histogram of oriented gradients for X-ray images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 5 (October 1, 2021): 4423. http://dx.doi.org/10.11591/ijece.v11i5.pp4423-4430.

Full text
Abstract:
<span>Lebesgue spaces (</span><em><span>L<sup>p</sup></span></em><span> over </span><em><span>R<sup>n</sup></span></em><span>) play a significant role in mathematical analysis. They are widely used in machine learning and artificial intelligence to maximize performance or minimize error. The well-known histogram of oriented gradients (HOG) algorithm applies the 2-norm (Euclidean distance) to detect features in images. In this paper, we apply different </span><em><span>p</span></em><span>-norm values to identify the impact that changing these norms has on the original algorithm. The aim of this modification is to achieve better performance in classifying X-ray medical images related to of COVID-19 patients. The efficiency of the </span><em><span>p</span></em><span>-HOG algorithm is compared with the original HOG descriptor using a support vector machine implemented in Python. The results of the comparisons are promising, and the </span><em><span>p</span></em><span>-HOG algorithm shows greater efficiency in most cases.</span>
APA, Harvard, Vancouver, ISO, and other styles
8

Wójcikowski, Marek. "Histogram of Oriented Gradients with Cell Average Brightness for Human Detection." Metrology and Measurement Systems 23, no. 1 (March 1, 2016): 27–36. http://dx.doi.org/10.1515/mms-2016-0012.

Full text
Abstract:
Abstract A modification of the descriptor in a human detector using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) is presented. The proposed modification requires inserting the values of average cell brightness resulting in the increase of the descriptor length from 3780 to 3908 values, but it is easy to compute and instantly gives ≈ 25% improvement of the miss rate at 10‒4 False Positives Per Window (FPPW). The modification has been tested on two versions of HOG-based descriptors: the classic Dalal-Triggs and the modified one, where, instead of spatial Gaussian masks for blocks, an additional central cell has been used. The proposed modification is suitable for hardware implementations of HOG-based detectors, enabling an increase of the detection accuracy or resignation from the use of some hardware-unfriendly operations, such as a spatial Gaussian mask. The results of testing its influence on the brightness changes of test images are also presented. The descriptor may be used in sensor networks equipped with hardware acceleration of image processing to detect humans in the images.
APA, Harvard, Vancouver, ISO, and other styles
9

SIDDIK, MUHAMMAD ARSYAD, LEDYA NOVAMIZANTI, and I. NYOMAN APRAZ RAMATRYANA. "Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN." ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika 7, no. 2 (May 24, 2019): 284. http://dx.doi.org/10.26760/elkomika.v7i2.284.

Full text
Abstract:
ABSTRAKKolesterol merupakan lemak yang berada di dalam darah yang dibutuhkan untuk pembentukan hormon dan sel baru. Kadar kolesterol normal harus kurang dari 200 mg/dL, namun jika di atas 240 mg/dL akan berisiko tinggi terkena penyakit stroke dan jantung koroner. Penelitian ini menghasilkan suatu sistem yang dapat mendeteksi kadar kolesterol seseorang melalui citra mata menggunakan metode iridologi dan image processing. Citra mata diperoleh dari pasien laboratorium klinik sebanyak 120 citra mata. Proses sistem diawali dengan mengolah citra mata dengan metode cropping, resize, dan segmentasi. Metode ekstaksi ciri menggunakan Histogram of Oriented Gradients (HOG), dan klasifikasi menggunakan Artificial Neural Network (ANN). Sistem dapat mendeteksi kadar kolesterol dengan tiga level klasifikasi, yaitu normal, berisiko kolesterol tinggi, dan kolesterol tinggi dengan tingkat akurasi sebesar 93% dan waktu komputasi 0,0862 detik.Kata kunci: citra mata, kadar kolesterol, Histogram of Oriented Gradients, Artificial Neural Network ABSTRACTCholesterol is fat in the blood that is needed for the formation of hormones and new cells. Normal cholesterol levels should be less than 200 mg / dL, but if above 240 mg / dL will be at high risk of stroke and coronary heart disease. This study produced a system that can detect a person's cholesterol levels through eye images using iridology and image processing methods. Eye images obtained from clinical laboratory patients were 120 eye images. The system process begins with processing eye images using the method of cropping, resizing, and segmentation. Feature extraction method uses Histogram of Oriented Gradients (HOG), and classification using Artificial Neural Network (ANN). The system can detect cholesterol levels with three levels of classification, namely normal, at high risk of cholesterol, and high cholesterol with an accuracy rate of 93% and computing time of 0.0862 seconds.Keywords: eye image, cholesterol level, Histogram of Oriented Gradients, Artificial Neural Network
APA, Harvard, Vancouver, ISO, and other styles
10

De Ocampo, Anton Louise Pernez, Argel Bandala, and Elmer Dadios. "Gabor-enhanced histogram of oriented gradients for human presence detection applied in aerial monitoring." International Journal of Advances in Intelligent Informatics 6, no. 3 (November 6, 2020): 223. http://dx.doi.org/10.26555/ijain.v6i3.514.

Full text
Abstract:
In UAV-based human detection, the extraction and selection of the feature vector are one of the critical tasks to ensure the optimal performance of the detection system. Although UAV cameras capture high-resolution images, human figures' relative size renders persons at very low resolution and contrast. Feature descriptors that can adequately discriminate between local symmetrical patterns in a low-contrast image may improve a human figures' detection in vegetative environments. Such a descriptor is proposed and presented in this paper. Initially, the acquired images are fed to a digital processor in a ground station where the human detection algorithm is performed. Part of the human detection algorithm is the GeHOG feature extraction, where a bank of Gabor filters is used to generate textured images from the original. The local energy for each cell of the Gabor images is calculated to identify the dominant orientations. The bins of conventional HOG are enhanced based on the dominant orientation index and the accumulated local energy in Gabor images. To measure the performance of the proposed features, Gabor-enhanced HOG (GeHOG) and other two recent improvements to HOG, Histogram of Edge Oriented Gradients (HEOG) and Improved HOG (ImHOG), are used for human detection on INRIA dataset and a custom dataset of farmers working in fields captured via unmanned aerial vehicle. The proposed feature descriptor significantly improved human detection and performed better than recent improvements in conventional HOG. Using GeHOG improved the precision of human detection to 98.23% in the INRIA dataset. The proposed feature can significantly improve human detection applied in surveillance systems, especially in vegetative environments.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Histogram of Oriented Gradients (HOG)"

1

Lienemann, Matthew A. "Automated Multi-Modal Search and Rescue using Boosted Histogram of Oriented Gradients." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1507.

Full text
Abstract:
Unmanned Aerial Vehicles (UAVs) provides a platform for many automated tasks and with an ever increasing advances in computing, these tasks can be more complex. The use of UAVs is expanded in this thesis with the goal of Search and Rescue (SAR), where a UAV can assist fast responders to search for a lost person and relay possible search areas back to SAR teams. To identify a person from an aerial perspective, low-level Histogram of Oriented Gradients (HOG) feature descriptors are used over a segmented region, provided from thermal data, to increase classification speed. This thesis also introduces a dataset to support a Bird’s-Eye-View (BEV) perspective and tests the viability of low level HOG feature descriptors on this dataset. The low-level feature descriptors are known as Boosted Histogram of Oriented Gradients (BHOG) features, which discretizes gradients over varying sized cells and blocks that are trained with a Cascaded Gentle AdaBoost Classifier using our compiled BEV dataset. The classification is supported by multiple sensing modes with color and thermal videos to increase classification speed. The thermal video is segmented to indicate any Region of Interest (ROI) that are mapped to the color video where classification occurs. The ROI decreases classification time needed for the aerial platform by eliminating a per-frame sliding window. Testing reveals that with the use of only color data iv and a classifier trained for a profile of a person, there is an average recall of 78%, while the thermal detection results with an average recall of 76%. However, there is a speed up of 2 with a video of 240x320 resolution. The BEV testing reveals that higher resolutions are favored with a recall rate of 71% using BHOG features, and 92% using Haar-Features. In the lower resolution BEV testing, the recall rates are 42% and 55%, for BHOG and Haar-Features, respectively.
APA, Harvard, Vancouver, ISO, and other styles
2

Chrápek, David. "Učení a detekce objektů různých tříd v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236481.

Full text
Abstract:
This paper is focused on object learning and recognizing in the image and in the image stream. More specifically on learning and recognizing humans or theirs parts in case they are partly occluded, with possible usage on robotic platforms. This task is based on features called Histogram of Oriented Gradients (HOG) which can work quite well with different poses the human can be in. The human is split into several parts and those parts are detected individually. Then a system of voting is introduced in which detected parts votes for the final positions of found people. For training the detector a linear SVM is used. Then the Kalman filter is used for stabilization of the detector in case of detecting from image stream.
APA, Harvard, Vancouver, ISO, and other styles
3

Olejár, Adam. "Měření výšky postavy v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-220426.

Full text
Abstract:
The aim of this paper is a summary of the theory necessary for a modification, detection of person and the height calculation of the detected person in the image. These information were then used for implementation of the algoritm. The first half reveals teoretical problems and solutions. Shows the basic methods of image preprocessing and discusses the basic concepts of plane and projective geometry and transformations. Then describes the distortion, that brings into the picture imperfections of optical systems of cameras and the possibilities of removing them. Explains HOG algorithm and the actual method of calculating height of person detected in the image. The second half describes algoritm structure and statistical evaluation.
APA, Harvard, Vancouver, ISO, and other styles
4

Dočekal, Martin. "Porovnání klasifikačních metod." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-403211.

Full text
Abstract:
This thesis deals with a comparison of classification methods. At first, these classification methods based on machine learning are described, then a classifier comparison system is designed and implemented. This thesis also describes some classification tasks and datasets on which the designed system will be tested. The evaluation of classification tasks is done according to standard metrics. In this thesis is presented design and implementation of a classifier that is based on the principle of evolutionary algorithms.
APA, Harvard, Vancouver, ISO, and other styles
5

Hussain, Sibt Ul. "Apprentissage machine pour la détection des objets." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00722632.

Full text
Abstract:
Le but de cette thèse est de développer des méthodes pratiques plus performantes pour la détection d'instances de classes d'objets de la vie quotidienne dans les images. Nous présentons une famille de détecteurs qui incorporent trois types d'indices visuelles performantes - histogrammes de gradients orientés (Histograms of Oriented Gradients, HOG), motifs locaux binaires (Local Binary Patterns, LBP) et motifs locaux ternaires (Local Ternary Patterns, LTP) - dans des méthodes de discrimination efficaces de type machine à vecteur de support latent (Latent SVM), sous deux régimes de réduction de dimension - moindres carrées partielles (Partial Least Squares, PLS) et sélection de variables par élagage de poids SVM (SVM Weight Truncation). Sur plusieurs jeux de données importantes, notamment ceux du PASCAL VOC2006 et VOC2007, INRIA Person et ETH Zurich, nous démontrons que nos méthodes améliorent l'état de l'art du domaine. Nos contributions principales sont : Nous étudions l'indice visuelle LTP pour la détection d'objets. Nous démontrons que sa performance est globalement mieux que celle des indices bien établies HOG et LBP parce qu'elle permet d'encoder à la fois la texture locale de l'objet et sa forme globale, tout en étant résistante aux variations d'éclairage. Grâce à ces atouts, LTP fonctionne aussi bien pour les classes qui sont caractérisées principalement par leurs structures que pour celles qui sont caractérisées par leurs textures. En plus, nous démontrons que les indices HOG, LBP et LTP sont bien complémentaires, de sorte qu'un jeux d'indices étendu qui intègre tous les trois améliore encore la performance. Les jeux d'indices visuelles performantes étant de dimension assez élevée, nous proposons deux méthodes de réduction de dimension afin d'améliorer leur vitesse et réduire leur utilisation de mémoire. La première, basée sur la projection moindres carrés partielles, diminue significativement le temps de formation des détecteurs linéaires, sans réduction de précision ni perte de vitesse d'exécution. La seconde, fondée sur la sélection de variables par l'élagage des poids du SVM, nous permet de réduire le nombre d'indices actives par un ordre de grandeur avec une réduction minime, voire même une petite augmentation, de la précision du détecteur. Malgré sa simplicité, cette méthode de sélection de variables surpasse toutes les autres approches que nous avons mis à l'essai.
APA, Harvard, Vancouver, ISO, and other styles
6

Kuřátko, Jiří. "Počítání lidí ve videu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2016. http://www.nusl.cz/ntk/nusl-255470.

Full text
Abstract:
This master's thesis prepared the programme which is able to follow the trajectories of the movement of people and based on this to create various statistics. In practice it is an effective marketing tool which can be used for instance for customer flow analyses, optimal evaluation of opening hours, visitor traffic analyses and for a lot of other benefits. Histograms of oriented gradients, SVM classificator and optical flow monitoring were used to solve this problem. The method of multiple hypothesis tracking was selected for the association data. The system's quality was evaluated from the video footage of the street with the large concentration of pedestrians and from the school's camera system, where the movement in the corridor was monitored and the number of people counted.
APA, Harvard, Vancouver, ISO, and other styles
7

Dvořák, Michal. "Detekce a rozpoznání dopravního značení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221299.

Full text
Abstract:
The goal of this thesis is the utilization of computer vision methods, in a way that will lead to detection and identification of traffic signs in an image. The final application is to analyze video feed from a video camcorder placed in a vehicle. With focus placed on effective utilization of computer resources in order to achieve real time identification of signs in a video stream.
APA, Harvard, Vancouver, ISO, and other styles
8

Vajhala, Rohith, Rohith Maddineni, and Preethi Raj Yeruva. "Weapon Detection In Surveillance Camera Images." Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13565.

Full text
Abstract:
Now a days, Closed Circuit Television (CCTV) cameras are installedeverywhere in public places to monitor illegal activities like armedrobberies. Mostly CCTV footages are used as post evidence after theoccurrence of crime. In many cases a person might be monitoringthe scene from CCTV but the attention can easily drift on prolongedobservation. Eciency of CCTV surveillance can be improved by in-corporation of image processing and object detection algorithms intomonitoring process.The object detection algorithms, previously implemented in CCTVvideo analysis detect pedestrians, animals and vehicles. These algo-rithms can be extended further to detect a person holding weaponslike rearms or sharp objects like knives in public or restricted places.In this work the detection of weapon from CCTV frame is acquiredby using Histogram of Oriented Gradients (HOG) as feature vector andarticial neural networks performing back-propagation algorithm forclassication.As a weapon in the hands of a human is considered to be greaterthreat as compared to a weapon alone, in this work the detection ofhuman in an image prior to a weapon detection has been found advan-tageous. Weapon detection has been performed using three methods.In the rst method, the weapon in the image is detected directly with-out human detection. Second and third methods use HOG and back-ground subtraction methods for detection of human prior to detectionof a weapon. A knife and a gun are considered as weapons of inter-est in this work. The performance of the proposed detection methodswas analysed on test image dataset containing knives, guns and im-ages without weapon. The accuracy rate 84:6% has been achievedby a single-class classier for knife detection. A gun and a knife havebeen detected by the three-class classier with an accuracy rate 83:0%.
APA, Harvard, Vancouver, ISO, and other styles
9

Němec, Jiří. "Detekce pohybujících se objektů ve video sekvenci." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-412865.

Full text
Abstract:
This thesis deals with methods for the detection of people and tracking objects in video sequences. An application for detection and tracking of players in video recordings of sport activities, e.g. hockey or basketball matches, is proposed and implemented. The designed application uses the combination of histograms of oriented gradients and classification based on SVM (Support Vector Machines) for detecting players in the picture. Moreover, a particle filter is used for tracking detected players. The whole system was fully tested and the results are shown in the graphs and tables with verbal descriptions.
APA, Harvard, Vancouver, ISO, and other styles
10

Memarzadeh, Milad. "Automated 2D Detection and Localization of Construction Resources in Support of Automated Performance Assessment of Construction Operations." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76908.

Full text
Abstract:
This study presents two computer vision based algorithms for automated 2D detection of construction workers and equipment from site video streams. The state-of-the-art research proposes semi-automated detection methods for tracking of construction workers and equipment. Considering the number of active equipment and workers on jobsites and their frequency of appearance in a camera's field of view, application of semi-automated techniques can be time-consuming. To address this limitation, two new algorithms based on Histograms of Oriented Gradients and Colors (HOG+C), 1) HOG+C sliding detection window technique, and 2) HOG+C deformable part-based model are proposed and their performance are compared to the state-of-the-art algorithm in computer vision community. Furthermore, a new comprehensive benchmark dataset containing over 8,000 annotated video frames including equipment and workers from different construction projects is introduced. This dataset contains a large range of pose, scale, background, illumination, and occlusion variation. The preliminary results with average performance accuracies of 100%, 92.02%, and 89.69% for workers, excavators, and dump trucks respectively, indicate the applicability of the proposed methods for automated activity analysis of workers and equipment from single video cameras. Unlike other state-of-the-art algorithms in automated resource tracking, these methods particularly detects idle resources and does not need manual or semi-automated initialization of the resource locations in 2D video frames.
Master of Science
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Histogram of Oriented Gradients (HOG)"

1

Nandi, Avishek, Paramartha Dutta, and Md Nasir. "Automatic Facial Expression Recognition Using Histogram Oriented Gradients (HoG) of Shape Information Matrix." In Intelligent Computing and Communication, 343–51. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1084-7_33.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Alekhya, Thanikonda, and S. Ranjan Mishra. "Object Recognition with Discriminately Trained Part-Based Model on HOG (Histogram of Oriented Gradients)." In Advances in Intelligent Systems and Computing, 645–53. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2526-3_67.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tambi, Priya, Sarika Jain, and Durgesh Kumar Mishra. "Person-Dependent Face Recognition Using Histogram of Oriented Gradients (HOG) and Convolution Neural Network (CNN)." In International Conference on Advanced Computing Networking and Informatics, 35–40. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2673-8_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Olejniczak, Michał, and Marek Kraft. "Taming the HoG: The Influence of Classifier Choice on Histogram of Oriented Gradients Person Detector Performance." In Artificial Intelligence and Soft Computing, 552–60. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59063-9_49.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zhao, Yong, and Yong-feng Ju. "A Family of Efficient Appearance Models Based on Histogram of Oriented Gradients (HOG), Color Histogram and Their Fusion for Human Pose Estimation." In Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications, 842–50. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03766-6_94.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Azam, Kazi Sultana Farhana, Farhin Farhad Riya, and Shah Tuhin Ahmed. "Leaf Detection Using Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Classifying with SVM Utilizing Claim Dataset." In Intelligent Data Communication Technologies and Internet of Things, 313–23. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9509-7_27.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Calvillo, Alberto Dzul, Roberto A. Vazquez, Jose Ambrosio, and Axel Waltier. "Face Recognition Using Histogram Oriented Gradients." In Intelligent Computing Systems, 125–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30447-2_11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Arora, Ridhi, and Parvinder Singh. "Histogram of Oriented Gradients for Image Mosaicing." In Innovations in Computational Intelligence, 211–25. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4555-4_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Kelly, Colm, Roger Woods, Moslem Amiri, Fahad Siddiqui, and Karen Rafferty. "Programmable Architectures for Histogram of Oriented Gradients Processing." In Handbook of Signal Processing Systems, 649–82. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91734-4_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Jangid, Mahesh, Sumit Srivastava, and Vivek Kumar Verma. "Real-Time Bottle Detection Using Histogram of Oriented Gradients." In Lecture Notes in Electrical Engineering, 118–25. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8240-5_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Histogram of Oriented Gradients (HOG)"

1

Arulananth, T. S., M. Sujitha, M. Nalini, B. Srividya, and K. Raviteja. "Fake shadow detection using local histogram of oriented gradients (HOG) features." In 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2017. http://dx.doi.org/10.1109/iceca.2017.8212765.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Lee, K. L., and M. M. Mokji. "Automatic target detection in GPR images using Histogram of Oriented Gradients (HOG)." In 2014 2nd International Conference on Electronic Design (ICED). IEEE, 2014. http://dx.doi.org/10.1109/iced.2014.7015795.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Patel, Hitanshu A., and Ritesh D. Rajput. "Smart Surveillance System Using Histogram of Oriented Gradients (HOG) Algorithm and Haar Cascade Algorithm." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697464.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Hannad, Yaâcoub, Imran Siddiqi, Youssef El Merabet, and Mohamed El Youssfi El Kettani. "Arabic Writer Identification System Using the Histogram of Oriented Gradients (HOG) of Handwritten Fragments." In the Mediterranean Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/3038884.3038900.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Hosotani, Daisuke, Ikushi Yoda, and Katsuhiko Sakaue. "Wheelchair recognition by using stereo vision and histogram of oriented gradients (HOG) in real environments." In 2009 Workshop on Applications of Computer Vision (WACV). IEEE, 2009. http://dx.doi.org/10.1109/wacv.2009.5403043.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Rosyidi, Lukman, Adrianto Prasetyo, and Muh Syaiful Romadhon. "Object Tracking with Raspberry Pi using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM)." In 2020 8th International Conference on Information and Communication Technology (ICoICT). IEEE, 2020. http://dx.doi.org/10.1109/icoict49345.2020.9166330.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Maraskolhe, Priyanka N., and A. S. Bhalchandra. "Analysis of Facial Expression Recognition using Histogram of Oriented Gradient (HOG)." In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2019. http://dx.doi.org/10.1109/iceca.2019.8821814.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Correa, Thays, Fabíola De Oliveira, Matheus Baffa, and Lucas Lattari. "Unsupervised Segmentation of Breast Infrared Images in Lateral View Using Histogram of Oriented Gradients." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13477.

Full text
Abstract:
Breast cancer is the second most common type of cancer in the world. It is estimated that 29.7% of new cases diagnosed in Brazil occur in any structures of the breasts. However, the disease has a good prognosis if detected early. Thus, the development of new technologies to help doctors to provide an accurate diagnosis is indispensable. The goal of this work is to develop a new method to automate parts of computer-aided diagnosis systems, performing the unsupervised segmentation of the Region of Interest (ROI) of infrared breast images acquired in lateral view. The segmentation proposed in this paper consists of three stages. The first stage pre-processes the infrared images of the lateral region of breasts. Later, features are extracted from a descriptor based on Histogram of Oriented Gradients (HOG). Concluding, a Machine Learning algorithm is used to perform the segmentation of the sample. The current method obtained an average of 89.9% accuracy and 94.3% specificity in our experiments, which is promising compared to other works.
APA, Harvard, Vancouver, ISO, and other styles
9

Tsai, Sam S., Huizhong Chen, David Chen, and Bernd Girod. "Word-HOGs: Word histogram of oriented gradients for mobile visual search." In 2014 IEEE International Conference on Image Processing (ICIP). IEEE, 2014. http://dx.doi.org/10.1109/icip.2014.7025806.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Uzun, Yucel, Muhammet Balcilar, Khudaydad Mahmoodi, Feruz Davletov, M. Fatih AmasyalI, and Sirma Yavuz. "Usage of HoG (histograms of oriented gradients) features for victim detection at disaster areas." In 2013 8th International Conference on Electrical and Electronics Engineering (ELECO). IEEE, 2013. http://dx.doi.org/10.1109/eleco.2013.6713903.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Histogram of Oriented Gradients (HOG)' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography