Relevant bibliographies by topics / Histogram of Optical Flow Gradients

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Academic literature on the topic 'Histogram of Optical Flow Gradients'

Author: Grafiati
Published: 11 December 2022
Last updated: 19 July 2025

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Journal articles on the topic "Histogram of Optical Flow Gradients"

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Gopakumar, K., and S. S. Suni. "Fusing pyramid histogram of gradients and optical flow for hand gesture recognition." International Journal of Computational Vision and Robotics 1, no. 1 (2020): 1. http://dx.doi.org/10.1504/ijcvr.2020.10027249.

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Suni, S. S., and K. Gopakumar. "Fusing pyramid histogram of gradients and optical flow for hand gesture recognition." International Journal of Computational Vision and Robotics 10, no. 5 (2020): 449. http://dx.doi.org/10.1504/ijcvr.2020.109396.

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Zhang, Jianguang, Yongxia Li, An Tai, Xianbin Wen, and Jianmin Jiang. "Motion Video Recognition in Speeded-Up Robust Features Tracking." Electronics 11, no. 18 (2022): 2959. http://dx.doi.org/10.3390/electronics11182959.

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Motion video recognition has been well explored in applications of computer vision. In this paper, we propose a novel video representation, which enhances motion recognition in videos based on SURF (Speeded-Up Robust Features) and two filters. Firstly, the detector scheme of SURF is used to detect the candidate points of the video because it is an efficient faster local feature detector. Secondly, by using the optical flow field and trajectory, the feature points can be filtered from the candidate points, which enables a robust and efficient extraction of motion feature points. Additionally, we introduce a descriptor, called MoSURF (Motion Speeded-Up Robust Features), based on SURF (Speeded-Up Robust Features), HOG (Histogram of Oriented Gradient), HOF (Histograms of Optical Flow), MBH(Motion Boundary Histograms), and trajectory information, which can effectively describe motion information and are complementary to each other. We evaluate our video representation under action classification on three motion video datasets namely KTH, YouTube, and UCF50. Compared with state-of-the-art methods, the proposed method shows advanced results on all datasets.
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Yamuna G., Karthika Pragadeeswari C. ,. "RIGID TRACKING FOR SCALE AND ROTATION VARYING TARGETS FROM MOVING CAMERA." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 175–80. http://dx.doi.org/10.17762/itii.v9i2.327.

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Targets when move rapidly needed to be tracked in many significant fields such as in combat applications. Objects undergoes many scale changes and also undergoes rotation variance. The target when viewed from static position, the size becomes smaller as the target moves farther and farther. Tracking the targets needs more attention and this can be done by Improved optical flow to which feature extraction through Histogram of Oriented Gradients and Random Sample Consensus (RANSAC) algorithm for scale and rotation invariance is added. The performance of the method is measured by its computation time, accuracy and high true positive values and other related parameters simulated in MAT LAB.
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Mota, V. F., E. A. Perez, L. M. Maciel, M. B. Vieira, and P. H. Gosselin. "A tensor motion descriptor based on histograms of gradients and optical flow." Pattern Recognition Letters 39 (April 2014): 85–91. http://dx.doi.org/10.1016/j.patrec.2013.08.008.

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Fan, Xijian, and Tardi Tjahjadi. "A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences." Pattern Recognition 48, no. 11 (2015): 3407–16. http://dx.doi.org/10.1016/j.patcog.2015.04.025.

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Hariyono, Joko, Van-Dung Hoang, and Kang-Hyun Jo. "Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/196415.

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This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells14×14pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.
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Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network.
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Jahagirdar, Aditi, and Rashmi Phalnikar. "Comparison of feed forward and cascade forward neural networks for human action recognition." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 892–99. https://doi.org/10.11591/ijeecs.v25.i2.pp892-899.

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Humans can perform an enormous number of actions like running, walking, pushing, and punching, and can perform them in multiple ways. Hence recognizing a human action from a video is a challenging task. In a supervised learning environment, actions are first represented using robust features and then a classifier is trained for classification. The selection of a classifier does affect the performance of human action recognition. This work focuses on the comparison of two structures of the neural network, namely, feed forward neural network and cascade forward neural network, for human action recognition. Histogram of oriented gradients (HOG) and histogram of optical flow (HOF) are used as features for representing the actions. HOG represents the spatial features of the video while HOF gives motion features of the video. The performance of two neural network architectures is compared based on recognition accuracy. Well-known publically available datasets for action and interaction detection are used for testing. It is seen that, for human action recognition applications, feed forward neural network gives better results in terms of higher recognition accuracy than Cascade forward neural network.
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Thimmegowda, Thirthe Gowda Mallinathapura, and Chandrika Jayaramaiah. "Cluster-based segmentation for tobacco plant detection and classification." Bulletin of Electrical Engineering and Informatics 12, no. 1 (2023): 75–85. http://dx.doi.org/10.11591/eei.v12i1.4388.

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Tobacco is one of the major economical crops in the agriculture sector. It is essential to detect tobacco plants using unmanned aerial vehicle (UAV) images for improved crop yield and plays an important role in the early treatment of tobacco plants. The proposed research work is carried out in three phases: In the first phase, we collect images from UAV’s and apply the French Commision Internationale de l'eclairage (CIE) L*a*b colour space model as pre-processing operations and segmentation. And then two prominent motion descriptors namely histogram of flow (HOF) and motion boundary histogram (MBH) are combined with the optimal histogram of oriented gradients (HOG) descriptor for exploring optimal motion trajectory and spatial measurements. And finally, the spatial variations with respect to the scale and illumination changes are incorporated using the optimal HOG descriptor. Here both dense motion patterns and HOG are refined using hierarchical feature selection using principal component analysis (PCA). The proposed model is trained and evaluated on different tobacco UAV image datasets and done a comparative analysis of different machine learning (ML) algorithms. The proposed model achieves good performance with 95% accuracy and 92% of sensitivity.
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Dissertations / Theses on the topic "Histogram of Optical Flow Gradients"

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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.

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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.
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Nallaivarothayan, Hajananth. "Video based detection of normal and anomalous behaviour of individuals." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/106947/1/Hajananth_Nallaivarothayan_Thesis.pdf.

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This PhD research has proposed novel computer vision and machine learning algorithms for the problem of video based anomalous event detection of individuals. Varieties of Hidden Markov Models were designed to model the temporal and spatial causalities of crowd behaviour. A Markov Random Field on top of a Gaussian Mixture Model is proposed to incorporate spatial context information during classification. Discriminative conditional random field methods are also proposed. Novel features are proposed to extract motion and appearance information. Most of the proposed approaches comprehensively outperform other techniques on publicly available datasets during the time of publications originating from the results.
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Klos, Dominik. "Počítání tlakových lahví v obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236055.

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This thesis deals with an automatic counting of cylinders placed on the back of a truck using images taken by a camera mounted above the car. To achieve this goal, an SVM classifier based on HOG image descriptors has been trained to detect the cylinders. Further, a tracking method based on optical flow estimation has been designed to track the cylinders through image sequences. The result of the thesis is an application that counts bottles with precision 93,08 % placed on the truck and visualizes results of the detection.
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Mohammed, Abdulmalik. "Obstacle detection and emergency exit sign recognition for autonomous navigation using camera phone." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/obstacle-detection-and-emergency-exit-sign-recognition-for-autonomous-navigation-using-camera-phone(e0224d89-e743-47a4-8c68-52f718457098).html.

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In this research work, we develop an obstacle detection and emergency exit sign recognition system on a mobile phone by extending the feature from accelerated segment test detector with Harris corner filter. The first step often required for many vision based applications is the detection of objects of interest in an image. Hence, in this research work, we introduce emergency exit sign detection method using colour histogram. The hue and saturation component of an HSV colour model are processed into features to build a 2D colour histogram. We backproject a 2D colour histogram to detect emergency exit sign from a captured image as the first task required before performing emergency exit sign recognition. The result of classification shows that the 2D histogram is fast and can discriminate between objects and background with accuracy. One of the challenges confronting object recognition methods is the type of image feature to compute. In this work therefore, we present two feature detectors and descriptor methods based on the feature from accelerated segment test detector with Harris corner filter. The first method is called Upright FAST-Harris and binary detector (U-FaHB), while the second method Scale Interpolated FAST-Harris and Binary (SIFaHB). In both methods, feature points are extracted using the accelerated segment test detectors and Harris filter to return the strongest corner points as features. However, in the case of SIFaHB, the extraction of feature points is done across the image plane and along the scale-space. The modular design of these detectors allows for the integration of descriptors of any kind. Therefore, we combine these detectors with binary test descriptor like BRIEF to compute feature regions. These detectors and the combined descriptor are evaluated using different images observed under various geometric and photometric transformations and the performance is compared with other detectors and descriptors. The results obtained show that our proposed feature detector and descriptor method is fast and performs better compared with other methods like SIFT, SURF, ORB, BRISK, CenSurE. Based on the potential of U-FaHB detector and descriptor, we extended it for use in optical flow computation, which we termed the Nearest-flow method. This method has the potential of computing flow vectors for use in obstacle detection. Just like any other new methods, we evaluated the Nearest flow method using real and synthetic image sequences. We compare the performance of the Nearest-flow with other methods like the Lucas and Kanade, Farneback and SIFT-flow. The results obtained show that our Nearest-flow method is faster to compute and performs better on real scene images compared with the other methods. In the final part of this research, we demonstrate the application potential of our proposed methods by developing an obstacle detection and exit sign recognition system on a camera phone and the result obtained shows that the methods have the potential to solve this vision based object detection and recognition problem.
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Book chapters on the topic "Histogram of Optical Flow Gradients"

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Rashwan, Hatem A., Mahmoud A. Mohamed, Miguel Angel García, Bärbel Mertsching, and Domenec Puig. "Illumination Robust Optical Flow Model Based on Histogram of Oriented Gradients." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40602-7_38.

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Schmid, Sönke, Daniel Tenbrinck, Xiaoyi Jiang, Klaus Schäfers, Klaus Tiemann, and Jörg Stypmann. "Histogram-Based Optical Flow for Functional Imaging in Echocardiography." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23672-3_58.

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Chon, Jaechoon, and Hyongsuk Kim. "Robust Fault Matched Optical Flow Detection Using 2D Histogram." In Computational Science and Its Applications - ICCSA 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11751595_123.

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Happy, S. L., and Aurobinda Routray. "Recognizing Subtle Micro-facial Expressions Using Fuzzy Histogram of Optical Flow Orientations and Feature Selection Methods." In Computational Intelligence for Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89629-8_13.

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Fuller, Gerald G. "Laser Doppler Velocimetry and Dynamic Light Scattering." In Optical Rheometry of Complex Fluids. Oxford University PressNew York, NY, 1995. http://dx.doi.org/10.1093/oso/9780195097184.003.0006.

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Abstract The methods described in this book are primarily concerned with the measurement of the microstructure of complex fluids subject to the application of external, orienting fields. In the case of flow, it is also of interest to measure the kinematics of the fluid motion. This chapter describes two experimental techniques that can be used for this purpose: laser Doppler velocimetry for the measurement of fluid velocities, and dynamic light scattering (or photon correlation spectroscopy) for the determination of velocity gradients.
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Kühl, Michael, and Niels Peter Revsbech. "Biogeochemical Microsensors For Boundary Layer Studies." In The Benthic Boundary Layer. Oxford University PressNew York, NY, 2001. http://dx.doi.org/10.1093/oso/9780195118810.003.0008.

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Abstract Interfacial processes in sediments, biofilms, and other benthic aquatic systems can be strongly affected by the presence of hydrodynamic boundary layers. The boundary layer (BL) can limit mass transfer between the overlying water and the solid matrix, thus affecting biological processes. The BL region adjacent to reactive surfaces that contains significant chemical gradients is known as the diffusive boundary layer (DBL). The DBL thickness is dependent on the flow velocity, surface topography and porosity of the solid matrix, and it ranges from <0.1 mm to several millimeters in thickness (see also chapters 5, 9, 14 and 15). As the interface is approached, mass transfer of solutes in the DBL becomes less dependent on advective motions (turbulence) and increasingly dependent on diffusion. Any net solute consumption or production within the matrix relative to the surrounding water, therefore, leads to the formation of concentration gradients over the DBL as well as within the matrix. The sediment-water interface thus can be the site of steep gradients of physicochemical variables over distances of <0.1 mm, and methods for studying these variables near the interface must resolve variations over very small distances without significant disturbance to these gradients. Noninvasive optical analysis, by direct microscopic observation, analysis of water current with laser-Doppler anemometry, nuclear magnetic resonance imaging (Wieland, 2000) or positron emission tomography (Khalili et al., 1998) fulfills these requirements, but most methods with high spatial resolution are based on microsensors.
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Kaimal, J. C., and J. J. Finnigan. "Sensors and Techniques for Observing the Boundary Layer." In Atmospheric Boundary Layer Flows. Oxford University Press, 1994. http://dx.doi.org/10.1093/oso/9780195062397.003.0009.

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Sensors used for boundary layer measurements fall into two broad categories: in situ sensors that can be mounted on the ground, on masts, towers, tethered balloons, free balloons, or aircraft; and remote sensors, ground-based or aircraft-mounted, that infer atmospheric properties through their effects on acoustic, microwave, and optical signals propagating through the air. In situ sensors are the traditional instruments of choice for surface and lower boundary layer studies, being the only ones capable of the accuracy and resolution needed for quantitative work. A major portion of this chapter will therefore be devoted to discussions of their characteristics. Remote sensors have the advantage of increased range and spatial scanning capability, but the constraints on minimum range and spatial resolution limit their usefulness for surface layer measurements. Used in combination, however, the two types of sensors provide a more complete description of the flow field being studied than either of the two can provide separately. New remote sensors with shorter minimum ranges and finer range resolutions are now becoming available for boundary layer applications. A brief discussion of such devices is also included in this chapter. The variables of greatest interest to boundary layer meteorologists are wind speed, temperature, humidity, and the fluxes of momentum, heat, mass, and radiant energy. Given suitable fast-response measurements of wind velocity and scalar fluctuations, we can calculate the eddy fluxes directly from the products of their fluctuating components as explained in Chapter 1. If only the gradients of their means are available, however, then over a flat homogeneous surface the fluxes may be inferred from the Monin-Obukhov relationships of Chapters 1 and 3. Practical methods for doing that are described in many texts; see, for example, Monteith (1975, 1976). (Those simple relationships do not hold, as we know, under advective conditions, in plant canopies, and over hills.) There are also sensors in use that measure surface and near-surface fluxes directly, such as the drag plate (surface stress), the lysimeter (latent heat flux), flux plates (soil heat flux), and radiometers (radiant heat flux). We will discuss these and a few other types as well because of their application to studies of plant canopies.
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Conference papers on the topic "Histogram of Optical Flow Gradients"

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Mohamed, Benaly, El Karch Hajar, Jariri Noura, El gouri Rachid, Hlou Lamaari, and Mezouari Abdelkader. "Strengthen Aircraft Real-Time Multiple Object Tracking with Optical Flow and Histogram of Oriented Gradient Provided HSMA Implemented in Low-Cost Energy VPU for UAV." In 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2023. http://dx.doi.org/10.1109/iraset57153.2023.10152953.

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Chen, Yan, Ling Zhang, Biyi Lin, Yong Xu, and Xiaobo Ren. "Fighting Detection Based on Optical Flow Context Histogram." In 2011 2nd International Conference on Innovations in Bio-Inspired Computing and Applications (IBICA). IEEE, 2011. http://dx.doi.org/10.1109/ibica.2011.28.

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Tahir, Muhammad Waseem, N. A. Zaidi, R. Blank, P. P. Vinayaka, M. J. Vellekoop, and W. Lang. "Detection of fungus through an optical sensor system using the histogram of oriented gradients." In 2016 IEEE SENSORS. IEEE, 2016. http://dx.doi.org/10.1109/icsens.2016.7808537.

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Yang, Zhijie, Tao Zhang, Jie Yang, Qiang Wu, Li Bai, and Lixiu Yao. "Violence detection based on histogram of optical flow orientation." In Sixth International Conference on Machine Vision (ICMV 13), edited by Branislav Vuksanovic, Jianhong Zhou, and Antanas Verikas. SPIE, 2013. http://dx.doi.org/10.1117/12.2051390.

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Cui, Shiyao, Nianqiang Li, and Zhen Liu. "Multi-directional crowded objects segmentation based on optical flow histogram." In 2011 4th International Congress on Image and Signal Processing (CISP). IEEE, 2011. http://dx.doi.org/10.1109/cisp.2011.6099914.

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Carvajal, Johanna, Conrad Sanderson, Chris McCool, and Brian C. Lovell. "Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients." In the MLSDA 2014 2nd Workshop. ACM Press, 2014. http://dx.doi.org/10.1145/2689746.2689748.

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Ge, Zhendi, Faliang Chang, and Hongbin Liu. "Multi-target tracking based on Kalman filtering and optical flow histogram." In 2017 Chinese Automation Congress (CAC). IEEE, 2017. http://dx.doi.org/10.1109/cac.2017.8243203.

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CHO, Y., L. CARR, and M. CHANDRASEKHARA. "Corrections to fringe distortion due to flow density gradients in optical interferometry." In 31st Aerospace Sciences Meeting. American Institute of Aeronautics and Astronautics, 1993. http://dx.doi.org/10.2514/6.1993-631.

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Qu, Zhi-yi, Ying Liu, Yan-min Liu, and Lin-na Zhang. "A Pornographic Videos Detection Method Based on Optical Flow Direction's Statistical Histogram." In 2009 International Symposium on Computer Network and Multimedia Technology (CNMT 2009). IEEE, 2009. http://dx.doi.org/10.1109/cnmt.2009.5374612.

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Pivezhandi, Mohammad, Phillip H. Jones, and Joseph Zambreno. "ParaHist: FPGA Implementation of Parallel Event-Based Histogram for Optical Flow Calculation." In 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE, 2020. http://dx.doi.org/10.1109/asap49362.2020.00038.

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