Relevant bibliographies by topics / AdaBoost Haar Vehicle Detection

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  2. Dissertations / Theses
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Academic literature on the topic 'AdaBoost Haar Vehicle Detection'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "AdaBoost Haar Vehicle Detection"

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Tian, M. Z., L. Liu, J. Y. Lu, and Y. Cheng. "Vehicle recognition based on Haar features and Adaboost cascade classifier." Journal of Physics: Conference Series 2303, no. 1 (2022): 012052. http://dx.doi.org/10.1088/1742-6596/2303/1/012052.

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Abstract With the progress of world science and economic development, more and more industries are developing towards intelligence and automation. In the field of intelligent driving, the intelligent vehicle environment perception method based on machine vision has become a hot research topic. Based on monocular vision system, aiming at the requirements of different target features and detection accuracy and efficiency, this paper improves the Haar feature and Adaboost cascade classifier recognition algorithm combined with gray symmetry method to adapt to the recognition environment required b
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Pan, Hao, and Bailing Zhang. "An Integrative Approach to Accurate Vehicle Logo Detection." Journal of Electrical and Computer Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/391652.

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Vehicle logo detection from images captured by surveillance cameras is an important step towards the vehicle recognition that is required for many applications in intelligent transportation systems and automatic surveillance. The task is challenging considering the small target of logos and the wide range of variability in shape, color, and illumination. A fast and reliable vehicle logo detection approach is proposed following visual attention mechanism from the human vision. Two prelogo detection steps, that is, vehicle region detection and a small RoI segmentation, rapidly focalize a small l
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Miao, Xiao Dong, Shun Ming Li, Min Xiang Wei, and Huan Shen. "Fast Pedestrian Detection for Intelligent Vehicle Based on FPGA and Monocular Vision." Advanced Materials Research 651 (January 2013): 784–88. http://dx.doi.org/10.4028/www.scientific.net/amr.651.784.

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This paper presents a fast pedestrian detection algorithm for intelligent vehicle based on FPGA architecture, using AdaBoost algorithm and Haar features. We describe the hardware design including image scaling, integral image generation, pipelined processing as well as classifier, and parallel processing multiple classifiers to accelerate the computational speed of the pedestrian detection system. The proposed architecture for pedestrian detection has been tested using Verilog HDL and implemented in Xilinx Virtex-5 FPGA. Its performance has been measured about 38 times than the equivalent soft
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Song, Moon Kyou, and Md Mostafa Kamal Sarker. "Modeling and Implementing Two-Stage AdaBoost for Real-Time Vehicle License Plate Detection." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/697658.

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License plate (LP) detection is the most imperative part of the automatic LP recognition system. In previous years, different methods, techniques, and algorithms have been developed for LP detection (LPD) systems. This paper proposes to automatical detection of car LPs via image processing techniques based on classifier or machine learning algorithms. In this paper, we propose a real-time and robust method for LPD systems using the two-stage adaptive boosting (AdaBoost) algorithm combined with different image preprocessing techniques. Haar-like features are used to compute and select features
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Pavani, K. "Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 897–910. http://dx.doi.org/10.47059/revistageintec.v11i2.1723.

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Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was
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Bai, Zhong Hao, Zhi Peng Ding, and Qiang Yan. "Study on the Method of Pedestrian Detection in Automobile Safety System." Advanced Materials Research 580 (October 2012): 118–21. http://dx.doi.org/10.4028/www.scientific.net/amr.580.118.

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In order to improve automobile active safety performance, and reduce the traffic accidents between pedestrians and vehicles, a pedestrian detection method combined with pedestrian contour features is proposed based on the combination of the reliable Adaboost and SVM. For the requirements of fast and accurate pedestrian detection system, ten types of haar-like features are given as the coarse features firstly, and which are trained through Adaboost cascade algorithm to ensure the system with a high detection speed. Then, the hog features of strong ability to distinguish pedestrians are selected
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Tamim, Mr. "An Enhancement in Accuracy of Vehicle Detection in Parking Areas Employing Haar-Like Features and AdaBoost Algorithm." PRZEGLĄD ELEKTROTECHNICZNY 1, no. 9 (2021): 137–40. http://dx.doi.org/10.15199/48.2021.09.27.

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Imanuddin, Imanuddin, Fachrid Alhadi, Raza Oktafian, and Ahmad Ihsan. "Deteksi Mata Mengantuk pada Pengemudi Mobil Menggunakan Metode Viola Jones." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 18, no. 2 (2019): 321–29. http://dx.doi.org/10.30812/matrik.v18i2.389.

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Computer Vision is one of the branches of Image processing science that allows a combination of human beings, such as identifying an object like an eye and taking a decision. Many of the face detection systems use the Viola Jones method as an object detection method. The method of Viola Jones is known by having high speed and accuracy because it is useful to combine several concepts such as (Haar Features, Integral Image, AdaBoost, and Cascade Classifier) into a major method for detecting objects. The programming language used in this study uses the MATLAB programming language to facilitate th
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Ibrahim, Furkan Ince, Yengin Ilker, and Sik Park Jang. "Designing Optimum Vehicle Tracking Systems Using CamShift Algorithm for Multi-Channel Video Surveillance Applications." Information, International Information Institute, Tokyo, Japan 18, no. 12 (2015): 5143–54. https://doi.org/10.5281/zenodo.1059265.

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Tracking systems should have sufficient speed and performance to attain the human standard of reactiveness, robustness and intrusiveness in real-time. This paper attempts to describe design of optimum multiple vehicle tracking system to overcome the performance challenges in multi-channel video surveillance applications. A re-designed CamShift method is applied to enhance the speed performance of the tracking task. The experimental work and its results indicated that proposed method has performance benefits compared to its conventional version. This paper would be beneficial for audiences who
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Mustafa, Rashed, Yang Min, and Dingju Zhu. "Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier." Scientific World Journal 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/753860.

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Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the syst
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Dissertations / Theses on the topic "AdaBoost Haar Vehicle Detection"

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Tamang, Sujan. "Empirical Evaluation of AdaBoost Method in Detecting Transparent and Occluded Objects." Youngstown State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1527599823503772.

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Plačko, Michal. "Sledování lidské postavy ve videosekvenci." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220561.

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This thesis deals with human body detection and gestures tracking in videosequences. First, processing of videosequences in general is described. Further, different methods of human body detection are described and represented by significant papers. The most of the attention is focused on detection by real AdaBoost algorithm based on Haar-like features and Edgelet features. The practical part starts with selection of method that is implemented in this thesis. This method is detection by real AdaBoost based on Haar-like features. Further, different options of videosequence processing in JAVA ar
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Arslan, Duygu. "Global Appearance Based Airplane Detection From Satellite Imagery." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614502/index.pdf.

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There is a rising interest in geospatial object detection due to not only the complexity of manual processing of such huge amount of data provided by high resolution satellite imagery but also for military application needs. A fundamental and yet state-of-the art approach for object detection is based on methods that utilize the global appearance. In such a holistic approach, the information of the object class is aimed to be modeled as a whole in the learning phase. And during the classification, a decision is taken at each window of the test image. In this thesis, two different discriminativ
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Nagavelli, Sai Krishnanand. "Improve Nano-Cube Detection Performance Using A Method of Separate Training of Sample Subsets." Youngstown State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1485267005121308.

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Kičina, Pavol. "Automatická identifikace tváří v reálných podmínkách." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218980.

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This master‘s thesis describes the identification faces in real terms. It includes an overview of current methods of detection faces by the classifiers. It also includes various methods for detecting faces. The second part is a description of two programs designed to identify persons. The first program operates in real time under laboratory conditions, where using web camera acquires images of user's face. This program is designed to speed recognition of persons. The second program has been working on static images, in real terms. The main essence of this method is successful recognition of pe
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Šašinka, Ondřej. "Detekce obličeje." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236684.

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This MSc Thesis deals with face detection in image. In this approach, facial features (eyes, nose, mouth corners) are detected first and then joined to the whole face. For the facial features detection, classifiers trained with AdaBoost algorithm are used. Haar wavelets are used as features for classification.
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Mašek, Jan. "Detekce objektů v obraze s pomocí Haarových příznaků." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219794.

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This thesis deals with the image object detection using Haar--like features and AdaBoost algorithm. The text describes methods how to train and test an object detector. The main contributon of this thesis consists in creation image object detector in Java programming language. Created algorithms were integrated as plugin into the RapidMiner tool, which is widely used and known worldwide as tool for data mining. The thesis contains the instructions for created operators and few exaples for executing in RapidMiner tool. The functionality of image object detector was demonstrated on selected medi
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Kamiya, Keitaro. "A framework of vision-based detection-tracking surveillance systems for counting vehicles." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45937.

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This thesis presents a framework for motor vehicle detection-tracking surveillance systems. Given an optimized object detection template, the feasibility and effectiveness of the methodology is considered for vehicle counting applications, implementing both a filtering operation of false detection, based on the speed variability in each segment of traffic state, and an occlusion handling technique which considers the unusual affine transformation of tracking subspace, as well as its highly fluctuating averaged acceleration data. The result presents the overall performance considering the trade
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Tureček, Martin. "Detekce obličejů v obraze z kamery na mobilním telefonu s WM." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-235553.

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This thesis deals with a face detection on mobile phones. It especially focuses on Windows Mobile platform. The introduction is therefore devoted to this operating system and alternatives of working with the camera. The next part of the text refers to general problems of the face detection in the image considering the weak performance of the target device. Another part of this thesis is a description of the acquisition of images from the camera using DirectShow multimedia framework and creation of a custom transformation filter for the face detection. Achieved results are summarized in the con
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Hruška, Martin. "Detekce charakteristických bodů obličeje v telerentgenovén snímku." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-218905.

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Description cephalometric images and the characteristic points on the skull for cephalometric analysis. Theoretical analysis of digital image editing and image before the actual detection. The range of possible methods for determining the characteristic points on the face. Experimental verification of edge detectors, Hu moments with neural networks and Haar wavelets with Viola-Jones detector.
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Book chapters on the topic "AdaBoost Haar Vehicle Detection"

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Martínez-Zarzuela, M., F. J. Díaz-Pernas, M. Antón-Rodríguez, F. Perozo-Rondón, and D. González-Ortega. "AdaBoost Face Detection on the GPU Using Haar-Like Features." In New Challenges on Bioinspired Applications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21326-7_36.

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Wang, Geng-hui, Ji-cai Deng, and Dong-bo Zhou. "Face Detection Technology Research Based on AdaBoost Algorithm and Haar Features." In Lecture Notes in Electrical Engineering. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4981-2_133.

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Mutsuddy, Aapan, Kaushik Deb, Tahmina Khanam, and Kang-Hyun Jo. "Illegally Parked Vehicle Detection Based on Haar-Cascade Classifier." In Intelligent Computing Theories and Application. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26969-2_57.

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Huang, Chih-Cheng, Chun-Yi Tsai, and Horng-Chang Yang. "An Extended Set of Haar-like Features for Bird Detection Based on AdaBoost." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-27183-0_17.

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Jiang, Tao, Mingdai Cai, Yuan Zhang, and Xiaodong Zhao. "A Fast Video Vehicle Detection Approach Based on Improved Adaboost Classifier." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61833-3_41.

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Sivaraja, Tulasii, and Abdullah Bade. "Digital Images Using Heuristic AdaBoost Haar Cascade Classifier Model, Detection of Partially Occluded Faces." In Encyclopedia of Computer Graphics and Games. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-08234-9_371-1.

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Niranjan, S., T. Jemima Jebaseeli, Samson Arun Raj, and S. Marshal. "Drowsiness Detection Using Adaboost Method and Haar Cascade Classifier to Improve Safety of Drivers." In Soft Computing: Theories and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2031-6_12.

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Sivaraja, Tulasii, and Abdullah Bade. "Digital Images Using Heuristic AdaBoost Haar Cascade Classifier Model, Detection of Partially Occluded Faces." In Encyclopedia of Computer Graphics and Games. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-23161-2_371.

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Kukade, Jyoti, Megha Patidar, Rahul S. Pawar, Trapti Mishra, Vidhya Barpha, and Prashant Panse. "Vehicle Detection Using Haar Features for Effective Traffic Management Using Machine Learning." In Learning and Analytics in Intelligent Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-9855-1_27.

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Frejlichowski, Dariusz, Katarzyna Gościewska, Paweł Forczmański, Adam Nowosielski, and Radosław Hofman. "Applying Image Features and AdaBoost Classification for Vehicle Detection in the ‘SM4Public’ System." In Image Processing and Communications Challenges 7. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23814-2_10.

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Conference papers on the topic "AdaBoost Haar Vehicle Detection"

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Naumov, I. I., R. R. Ibadov, and D. G. Chebotarev. "Haar Feature Based Object Detection Using Adaboost Classifier." In 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2025. https://doi.org/10.1109/icieam65163.2025.11028378.

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Lim, Na-Young, Jeon-Hyeok Lee, and Tae-Hyoung Park. "Long-Range Vehicle Detection of LiDAR using Adaboost Classification*." In 2025 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2025. https://doi.org/10.1109/sii59315.2025.10871122.

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Shujuan, Sun, Xu Zhize, Wang Xingang, Huang Guan, Wu Wenqi, and Xu De. "Real-time vehicle detection using Haar-SURF mixed features and gentle AdaBoost classifier." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162227.

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JABRI, S., M. SAIDALLAH, A. EL BELRHITI EL ALAOUI, and A. EL FERGOUGUI. "Moving Vehicle Detection Using Haar-like, LBP and a Machine Learning Adaboost Algorithm." In 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS). IEEE, 2018. http://dx.doi.org/10.1109/ipas.2018.8708898.

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Zhu, Jie, and Zhiqian Chen. "Real Time Face Detection System Using Adaboost and Haar-like Features." In 2015 2nd International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2015. http://dx.doi.org/10.1109/icisce.2015.95.

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Li, Zhijiang, Chuan Dong, Ling Zheng, and Long Liu. "Traffic Signs Detection Based on Haar-Like Features and Adaboost Classifier." In Second International Conference on Transportation Information and Safety. American Society of Civil Engineers, 2013. http://dx.doi.org/10.1061/9780784413036.152.

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Zhang, Caili, Guangwen Liu, Xinli Zhu, and Hua Cai. "Face Detection Algorithm Based on Improved AdaBoost and New Haar Features." In 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2019. http://dx.doi.org/10.1109/cisp-bmei48845.2019.8965841.

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Hiromoto, Masayuki, Kentaro Nakahara, Hiroki Sugano, Yukihiro Nakamura, and Ryusuke Miyamoto. "A Specialized Processor Suitable for AdaBoost-Based Detection with Haar-like Features." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383415.

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Songyan Ma and Lu Bai. "A face detection algorithm based on Adaboost and new Haar-Like feature." In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS). IEEE, 2016. http://dx.doi.org/10.1109/icsess.2016.7883152.

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Kimura, Motoki, Janarbek Matai, Matthew Jacobsen, and Ryan Kastner. "A low-power Adaboost-based object detection processor using Haar-like features." In 2013 IEEE Third International Conference on Consumer Electronics ¿ Berlin (ICCE-Berlin). IEEE, 2013. http://dx.doi.org/10.1109/icce-berlin.2013.6697982.

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