Learning to Localize Objects with Structured Output Regression

Histograms of Oriented Gradients for Human Detection

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Rapid object detection using a boosted cascade of simple features

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SURF: Speeded Up Robust Features

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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

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A discriminatively trained, multiscale, deformable part model

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The PASCAL visual object classes challenge 2006 (VOC2006) results

Everingham, M., Zisserman, A., Williams, C. K. I., & Gool, L. V. (2006) · Cited by 2,079

Support vector machine learning for interdependent and structured output spaces

Tsochantaridis, I., Hofmann, T., Joachims, T., & Altun, Y. (2004) · International Conference on Machine Learning · doi:10.1145/1015330.1015341 · Cited by 1,493

Representing shape with a spatial pyramid kernel

Bosch, A., Zisserman, A., & Muñoz, X. (2007) · ACM International Conference on Image and Video Retrieval (CIVR) · doi:10.1145/1282280.1282340 · Cited by 1,423

Sampling Strategies for Bag-of-Features Image Classification

Nowak, E., Jurie, F., & Triggs, B. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744085_38 · Cited by 927

Combined Object Categorization and Segmentation With An Implicit Shape Model

Leibe, B., Leonardis, A., & Schiele, B. (2004) · Cited by 810

Active learning with version spaces for object detectionPreprint

Roy, S. D., Namboodiri, V. P., & Biswas, A. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07285 · Cited by 6

Object detection in surveillance video from dense trajectories

Zhai, M., Chen, L., Li, J., Khodabandeh, M., & Mori, G. (2015) · Machine Vision Applications · doi:10.1109/mva.2015.7153248 · Cited by 5

Object tracking with temporal prediction and spatial refinement (TPSR)

Gan, W., Lee, M., Wu, C.-H., & Kuo, C. ‐. J. (2016) · Signal Processing Image Communication · doi:10.1016/j.image.2016.07.005 · Cited by 5

Object detection using openCV with Python

(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets41586 · Cited by 5

A robust multilevel segment description for multi-class object recognition

Mostajabi, M., & Gholampour, I. (2014) · Machine Vision and Applications · doi:10.1007/s00138-014-0642-1 · Cited by 5

Hierarchical Image-Region Labeling via Structured Learning

McAuley, J., de Campos, T. E., Csurka, G., & Perronnin, F. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.49 · Cited by 5

Batch Normalization based Convolutional Block YOLOv3 Real Time Object Detection of Moving Images with Backdrop Adjustment

Umanandhini, D., Devi, M. S., Jabaseeli, N. B., & Sridevi, S. (2023) · International Conference on Smart Computing and Communications (ICSCC) · doi:10.1109/icscc59169.2023.10334988 · Cited by 4

Lightweight SDENet Fusing Model-Based and Learned Features for Computational Histopathology

Bao, R., Zhao, Y., Srivastava, A., Frazier, S. R., & Palaniappan, K. (2024) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2024.3470312 · Cited by 4

Multi-label multi-instance learning with missing object tags

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Pedestrian Localization

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Multi-Thread Approach to Object Detection Using YOLOv3

Abri, R., Abri, S., Yarıcı, A., & Çetin, S. (2020) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/icievicivpr48672.2020.9306644 · Cited by 4

Class-Specified Segmentation with Multi-scale Superpixels

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Realtime object detection in IoT (Internet of Things) devices

Arıbaş, E., & Dağlarlı, E. (2017) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu.2017.7960690 · Cited by 4

Atomic force microscope tip localization and tracking through deep learning based vision inside an electron microscope

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Real Time Object Detection Using YoloReal Time Object Detection Using Yolo

Ankith, I. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.39044 · Cited by 4

Regressing Local to Global Shape Properties for Online Segmentation and Tracking

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Towards automatic bounding box annotations from weakly labeled images

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Search-space sorting with hidden Markov models for occluded object recognition

Peña, F. A. G., & Vasconcelos, G. C. (2016) · IEEE International Conference on Intelligent Systems · doi:10.1109/is.2016.7737478 · Cited by 4

Looking out of the window: object localization by joint analysis of all windows in the image.Preprint

Vezhnevets, A. S., & Ferrari, V. (2015) · arXiv (Cornell University) · Cited by 4

Part Context Learning for Visual Tracking

Zhu, G., Wang, J., Zhao, C., & Lu, H. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.25 · Cited by 3

Vehicle Detection Using Local Size-Specific Classifiers

Noh, S., & Jeon, M. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edp7101 · Cited by 4

Learn to focus on objects for visual detection

Chen, Z., Li, J., & You, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.082 · Cited by 3

Power Efficient Object Detector with an Event-Driven Camera for Moving Object Surveillance on an FPGA

Shimoda, M., Sato, S., & Nakahara, H. (2019) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2018rcp0005 · Cited by 3

Drone detection using YOLO

Patil, S., Jaybhaye, S. M., Khalifa, M. M., Kharche, S., Khatib, A., & Kshirsagar, A. (2023) · AIP conference proceedings · doi:10.1063/5.0181506 · Cited by 3

Facemask Detection using the YOLO-v5 Algorithm: Assessing Dataset Variation and R esolutions

Kurniawan, F., Astawa, I. N. G. A., Atmaja, I. M. A. D. S., & Wibawa, A. P. (2023) · Register Jurnal Ilmiah Teknologi Sistem Informasi · doi:10.26594/register.v9i2.3249 · Cited by 3

Object Detection the Usage of YOLOV5: A Deep Learning Approach

Vuddanti, S., Rahul, G. S., Joel, D. J., Jaswanth, G., & Varun, C. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-7137-4_60 · Cited by 3

FINE-GRAINED AND LAYERED OBJECT RECOGNITION

Wu, Y., Zheng, N., Liu, Y., & Yuan, Z. (2012) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001412550063 · Cited by 3

Approximated Slack Scaling for Structural Support Vector Machines in Scene Depth Analysis

Liu, S., Zhai, B., Chan, S., Li, F., & Zhan, Y. (2013) · Mathematical Problems in Engineering · doi:10.1155/2013/817496 · Cited by 3

Robust Method of Vote Aggregation and Proposition Verification for Invariant Local Features

Kurzejamski, G., Zawistowski, J., & Sarwas, G. (2015) · International Conference on Computer Vision Theory and Applications · doi:10.5220/0005267002520259 · Cited by 3

Multi-Object Model-Free Tracking with Joint Appearance and Motion Inference

Liu, C., Yao, R., Rezatofighi, S. H., Reid, I. D., & Shi, Q. (2017) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2017.8227468 · Cited by 3

Structured Prediction for Event Detection

Hoai, M., & la Torre, F. D. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9969.003.0016 · Cited by 3

Co-occurrence Random Forests for Object Localization and Classification

Chu, Y.-W., & Liu, T.-L. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12297-2_60 · Cited by 3

Semantic Image Understanding: From Pixel to Word

Fu, H. (2012) · UPT. Syiah Kuala University Library (Syiah Kuala University) · Cited by 3

Exploiting context aware category discovery for image labeling

Liu, H., & Qu, Y. (2011) · International Conference on Internet Multimedia Computing and Service · doi:10.1145/2043674.2043707 · Cited by 2

A multi-scale vehicle tracking algorithm based on Structured Output SVM

Wei, P., Zhu, Y., Zuo, X., & Lu, M. (2015) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2015.7407840 · Cited by 2

Learning binary code features for UAV target tracking

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Power Efficient Object Detector with an Event-Driven Camera on an FPGA

Shimoda, M., Sato, S., & Nakahara, H. (2018) · International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART) · doi:10.1145/3241793.3241803 · Cited by 2

Proposed Model for Detection and Classification of Vehicles in Real-Time Video Based on Deep Learning

Jawad, D. J., Ogla, R., & Rahma, A. M. S. (2022) · ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS) · doi:10.1109/icetsis55481.2022.9888858 · Cited by 2

The development of a new edge computing device based on deep learning YOLOv8 algorithm for diabetic retinopathy Detection

Singh, D. P., Mohan, L., Pant, J., Mittal, A., & Dobhal, D. C. (2023) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n60023.2023.10541599 · Cited by 2

OBJECT DETECTION USING YOLO ALGORITHM

(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets60812 · Cited by 2

Robust visual tracking via adaptive kernelized correlation filter

Wang, B., Wang, D., & Liao, Q. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2261944 · Cited by 2

Efficient Structured Support Vector Regression

Jia, K. X., Wang, L., & Liu, N. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19318-7_46 · Cited by 2

Automated Foveola Localization in Retinal 3D-OCT Images Using Structural Support Vector Machine Prediction

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Investigation of segmentation based pooling for image quantification

Porter, R. B., Harvey, N. R., & Ruggiero, C. E. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2038707 · Cited by 2

Branch&Rank for Efficient Object Detection

Lehmann, A., Gehler, P., & Gool, L. V. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0670-8 · Cited by 2

Detecting optic disk based on structured learning

Fan, Z., Rong, Y., Cai, X., Li, W., Lin, H., Yu, Z., & Lu, J. (2015) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio.2015.7418923 · Cited by 2

Action Detection by Fusing Hierarchically Filtered Motion with Spatiotemporal Interest Point Features

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Structured output tracking guided by keypoint matching

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Optimizing the Measure of Performance

Keshet, J. (2014) · The MIT Press eBooks · doi:10.7551/mitpress/9969.003.0013 · Cited by 2

Structured Prediction for Event Detection

Nowozin, S., Gehler, P., Jancsary, J., & Lampert, C. H. (2014) · Cited by 2

Object Tracking Under Occlusion Using LGEM-Trained SSVM

Lin, A., Wei, T., & Ng, W. W. Y. (2018) · International Conference on Wavelet Analysis and Pattern Recognition · doi:10.1109/icwapr.2018.8521283 · Cited by 2

An efficient novel paradigm for object detection through web camera using deep learning (YOLOv5’s object detection model)

Chidananda, K., Naik, M. G., Mohan, Y. K., Madhavan, N., Arfan, S. A., & Kativarapu, A. (2023) · E3S Web of Conferences · doi:10.1051/e3sconf/202339101093 · Cited by 2

Stochastic functional descent for learning Support Vector Machines

He, K. (2014) · OpenBU (Boston University) · Cited by 2

Localized Structured PredictionPreprint

Ciliberto, C., Bach, F., & Rudi, A. (2018) · arXiv (Cornell University) · Cited by 2

Asynchronous high-speed feature extraction image sensor

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Structured svm backpropagation to convolutional neural network applying to human pose estimation

Witoonchart, P. (2016) · doi:10.58837/chula.the.2016.1476 · Cited by 2

Kernel Methods in Computer Vision: Object Localization, Clustering, and Taxonomy Discovery

Blaschko, M. B. (2009) · DepositOnce · doi:10.14279/depositonce-2166 · Cited by 2

Object Detection Algorithms for Digital Imaging Applications: A Review

Bello, R.-W., Owolawi, P. A., Wyk, E. V., & Tu, C. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1010205 · Cited by 1

Improved YOLOv5 for Efficient Elimination of Unwanted Video Frames From High‐Speed Video Arrays Capturing Bat's Kinematics

Jayathunga, D. P., Ramashini, M., Zaini, J., Müller, R., & Silva, L. C. D. (2025) · Applied AI Letters · doi:10.1002/ail2.70014 · Cited by 1

Progressive Pedestrian Localization Using Neural Networks

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Joint Kernel Learning for Supervised Image Segmentation

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Online structured hough forests for visual tracking

Qin, T., Zhong, B., & Wang, H. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6638070 · Cited by 1

Robust tracking via online Max-Margin structural learning with approximate sparse intersection kernel

Yao, R., Xia, S., & Zhou, Y. Z. (2014) · Neurocomputing · doi:10.1016/j.neucom.2014.08.001 · Cited by 1

Spatial-Visual Label Propagation for Local Feature Classification

El-Gaaly, T., Torki, M., & Elgammal, A. (2014) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2014.589 · Cited by 1

Output Feature Augmented Lasso

Zhang, C., Han, Y., Guo, X., & Cao, X. (2014) · Proceedings · doi:10.1109/icdm.2014.103 · Cited by 1

Visual Object Localization in Image Collections

Qu, Y., & Liu, H. (2011) · International Conference on Image and Graphics · doi:10.1109/icig.2011.123 · Cited by 1

Heterogeneous feature fusion for visual recognition

Cao, L. (2011) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 1

Long-term object tracking combined offline with online learning

Hu, M., Wei, Z., & Zhang, G. (2014) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2073119 · Cited by 1

Statistical Degradation Analysis for Real-Time Tracking in Severely Degraded Videos

Feng, Y., Liu, S., Wang, C., Ying, G., Yin, K., & Chen, S. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7305-2_36 · Cited by 1

Crowd scenes analysis using multiple sliding windows classifiers and Histogram of Oriented Gradient

Shalileh, S., & Shahdi, S. O. (2017) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/iranianmvip.2017.8342365 · Cited by 1

Analysis of Object Detection Algorithms Using Neural Networks

Ahluwalia, A. S., Sondhi, A., & Gupta, S. (2021) · Algorithms for intelligent systems · doi:10.1007/978-981-33-4087-9_53 · Cited by 1

Object Detection Using YOLO Algorithm

Biju, A. C., George, A. K., & Vignesh, K. H. (2022) · Kristu Jayanti Journal of Computational Sciences (KJCS) · doi:10.59176/kjcs.v2i1.2219 · Cited by 2

Delineating individual alluvial fans and morphological analysis based on digital elevation models

Zhou, X., Chen, Y., Sun, W., Yang, X., Wang, Y., Li, C., & Tang, G. (2025) · Geomorphology · doi:10.1016/j.geomorph.2025.109629 · Cited by 1

An $$\mathcal {O}(n \log n)$$ Cutting Plane Algorithm for Structured Output Ranking

Blaschko, M. B., Mittal, A., & Rahtu, E. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11752-2_11 · Cited by 1

Hippocampal segmentation using structured extreme learning machine with bag of features

Zaini, M. H. M., Shapiai, M. I., Mohamed, A. R., Fauzi, H., Ibrahim, Z., & Adam, A. (2017) · International Conference on Robotics, Automation and Sciences (ICORAS) · doi:10.1109/icoras.2017.8308075 · Cited by 1

Structured Output Prediction Using Privileged Information

Zhang, C., Sun, S., Tian, Y., & Wang, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2927693 · Cited by 1

Deep Learning Technique for Serving Visually Impaired Person

Chandankhede, P., & Kumar, A. (2019) · International Conference on Emerging Trends in Engineering and Technology (ICETET) · doi:10.1109/icetet-sip-1946815.2019.9092234 · Cited by 1

Input/Output Deep Architecture for Structured Output Problems.Preprint

Belharbi, S., Chatelain, C., Hérault, R., & Adam, S. (2015) · arXiv (Cornell University) · Cited by 1

Deep Neural Networks Regularization for Structured Output PredictionPreprint

Belharbi, S., Hérault, R., Chatelain, C., & Adam, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.07550 · Cited by 1

Looking outside of the Box: Object Detection and Localization with Multi-scale Patterns.Preprint

Ohn-Bar, E., & Trivedi, M. M. (2015) · arXiv (Cornell University) · Cited by 1

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