Conferences related to Video Tracking

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and severalco-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students, academics and industry researchers.

  • 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conferenceand 27co-located workshops and short courses. With its high quality and low cost, it provides anexceptional value for students,academics and industry.

  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    computer, vision, pattern, cvpr, machine, learning

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. Main conference plus 50 workshop only attendees and approximately 50 exhibitors and volunteers.

  • 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    CVPR is the premiere annual Computer Vision event comprising the main CVPR conference and 27 co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry.

  • 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Topics of interest include all aspects of computer vision and pattern recognition including motion and tracking,stereo, object recognition, object detection, color detection plus many more

  • 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Sensors Early and Biologically-Biologically-inspired Vision, Color and Texture, Segmentation and Grouping, Computational Photography and Video

  • 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics, motion analysis and physics-based vision.

  • 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    Concerned with all aspects of computer vision and pattern recognition. Issues of interest include pattern, analysis, image, and video libraries, vision and graphics,motion analysis and physics-based vision.

  • 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2006 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • 2005 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


GLOBECOM 2020 - 2020 IEEE Global Communications Conference

IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.


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Periodicals related to Video Tracking

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Biomedical Engineering, IEEE Reviews in

The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


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Most published Xplore authors for Video Tracking

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Xplore Articles related to Video Tracking

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The visual-based robust model predictive control for two-DOF video tracking system

2016 Chinese Control and Decision Conference (CCDC), 2016

In this paper, a visual-based control law for a two-rotational degrees-of- freedom (DOF) video tracking system, with robust model predictive control method was proposed. The video tracking system is derived as a visual servoing model whose parameters variation in the image Jacobian matrix can affect both stability and performance. By transforming this system into a convex combination of linear time-invariant ...


Video Tracking Based on Template Matching and Particle Filter

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018

In recent years, object tracking is still a challenging problem although many approaches have been successfully proposed. We propose a video tracking based on template matching and particle filter, for solving some issues in object tracking. The proposed method includes template matching and particles weighting. The object can be successfully tracked by template matching, except for some challenging sequences. To ...


A video tracking algorithm for UAV based on differential evolution particle filter

Proceedings of the 31st Chinese Control Conference, 2012

Video and image tracking algorithm is critical to unmanned aerial vehicle (UAV) tracking mission. In order to overcome the performance bottleneck of the conventional video tracking algorithm, a new video tracking algorithm based on differential evolutionary particle filter is proposed. It substantially improves standard particle filter via using the crossover and mutation operators, the new video tracking algorithm integrates the ...


A video tracking method based on Niche Particle Swarm Algorithm-Particle Filter

Proceedings of the 10th World Congress on Intelligent Control and Automation, 2012

In order to improve the stability and robustness in video tracking based on particle filter. We proposed Niche Particle Swarm Algorithm-Particle Filter (NPSA-PF) which applies Niche Particle Swarm Algorithm to the re-sampling stage in particle filter. The ability of Niche Particle Swarm Algorithm which improves the particles' local search ability and weakens the information sharing between particles, effectively reduces the ...


Automatic Control Method to Video Tracking System for a Flying Target

2016 International Conference on Information System and Artificial Intelligence (ISAI), 2016

In this paper, an automatic control method is proposed for a two rotational degrees-of-freedom (DOF) video tracking system to track a flying object. An acoustic passive localization algorithm based on planar five-element array is adopted, to focus on the flying target when it first appears. The kinematic of the video tracking system is analysed. Then the interaction matrix based on ...


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Educational Resources on Video Tracking

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IEEE-USA E-Books

  • The visual-based robust model predictive control for two-DOF video tracking system

    In this paper, a visual-based control law for a two-rotational degrees-of- freedom (DOF) video tracking system, with robust model predictive control method was proposed. The video tracking system is derived as a visual servoing model whose parameters variation in the image Jacobian matrix can affect both stability and performance. By transforming this system into a convex combination of linear time-invariant vertices form with the tensor-product (TP) model transformation method, the video tracking system is represented as a polytopic linear parameter-varying (LPV) system. The control signal is calculated online by carrying out the convex optimization involving linear matrix inequalities (LMIs) in MPC to guarantee the closed-loop stability and system constraints. The simulation results show that the proposed controller can effectively track the moving object with unknown velocity and distance information.

  • Video Tracking Based on Template Matching and Particle Filter

    In recent years, object tracking is still a challenging problem although many approaches have been successfully proposed. We propose a video tracking based on template matching and particle filter, for solving some issues in object tracking. The proposed method includes template matching and particles weighting. The object can be successfully tracked by template matching, except for some challenging sequences. To compensate for template matching, we exploit particle filter with Speeded Up Robust Features (SURF) to repair the failed tracking. Experimental results of the effectiveness and robustness are demonstrated, and the comparative performance is also shown.

  • A video tracking algorithm for UAV based on differential evolution particle filter

    Video and image tracking algorithm is critical to unmanned aerial vehicle (UAV) tracking mission. In order to overcome the performance bottleneck of the conventional video tracking algorithm, a new video tracking algorithm based on differential evolutionary particle filter is proposed. It substantially improves standard particle filter via using the crossover and mutation operators, the new video tracking algorithm integrates the differential evolutionary particle filter and the color histogram character. Simulation results show that the new algorithm performs well for UAV tracking problems.

  • A video tracking method based on Niche Particle Swarm Algorithm-Particle Filter

    In order to improve the stability and robustness in video tracking based on particle filter. We proposed Niche Particle Swarm Algorithm-Particle Filter (NPSA-PF) which applies Niche Particle Swarm Algorithm to the re-sampling stage in particle filter. The ability of Niche Particle Swarm Algorithm which improves the particles' local search ability and weakens the information sharing between particles, effectively reduces the tracking particles number and improves tracking stability and robustness. We use it in video tracking and the performance is validated to be effective.

  • Automatic Control Method to Video Tracking System for a Flying Target

    In this paper, an automatic control method is proposed for a two rotational degrees-of-freedom (DOF) video tracking system to track a flying object. An acoustic passive localization algorithm based on planar five-element array is adopted, to focus on the flying target when it first appears. The kinematic of the video tracking system is analysed. Then the interaction matrix based on the target centroid feature is derived for the target with irregular and complicated shape. Finally a predictive control approach is developed to facilitate an effective tracking. The experimental results show the effectiveness of the proposed control scheme.

  • Geometrically oriented video tracking

    In this paper one presents the video tracking problem as part of the complex task of video surveillance. A formal consideration of video tracking problem for single and multi-target case have been presented.

  • VCF: Velocity Correlation Filter, Towards Space-Borne Satellite Video Tracking

    Tracking a moving target of interests from a space-borne satellite video is really a difficulty, since the target usually occupies only a few pixels in each frame of satellite video. Even it is a long train. Most state-of-the-art tracking algorithms mainly rely on luminance or color features, failing to handle this video tracking problem due to the extremely inadequate quality of targets features. To overcome this difficulty, we propose a velocity correlation filter (VCF), employing velocity feature and inertia mechanism to construct a kernel correlation filter for satellite video targets tracking. The velocity feature has a high discriminative ability to detect moving targets in satellite videos and the inertia mechanism can prevent model drift adaptively. Experimental results on three real satellite video datasets show our proposed approach outperforms state-of-the-art tracking methods with a more than 100 frames per second.

  • Design of high-speed real-time processing platform faced on video tracking

    A miniature high-speed real-time video tracking platform which is based on the core of FPGA+DSP has been developed. The platform adopts I2C bus, link-port data transfer protocol and multi-DSP high-speed video processing. Through the combined use of DSP and FPGA, the platform has the following characteristics: high integration, strong computing power, flexible structure, good expansibility and so on, which reduce the complexity of design, improve the capacity of integrated processing, and can be applied in real-time video information processing effectively. Experimental results show that the system can capture video image(640 × 480 pixels) and complete track processing at the rate of 65 frames per sec, exceeding the conventional video rate of 30 frames per sec, hence meet the real-time video processing requirements.

  • Video Tracking via Tensor Neighborhood Preserving Discriminant Embedding

    In a real surveillance scenario, tracking an object usually interfered by the background information. To deal with this problem, this paper proposed a video tracking algorithm based on tensor neighborhood preserving discriminant embedding. The neighborhood relationships of an object within object class and background class are reasonable described by the object image patches similarities which are defined by histograms of oriented gradients. In order to distinguish between the object and background, we formulate an discriminant objective function that maximizing the scatters of object within object class while minimizing the scatters of object with background class, meanwhile maintaining the same neighborhood topological structure in lower dimensional tensor subspace. Finally, we can get the optimal estimate of the object state through Bayesian estimation framework. Experimental evaluations against two state-of-the-art tracking methods demonstrate the robustness and effectiveness of the proposed algorithm.

  • A study of fish velocity measurement base on video tracking

    We can research the impact of water quality on fish activity by tracking and measuring the fish swim speed. In order to achieve the tracking and test the velocity to the fish movement under the complex background, the paper takes the method of video tracking algorithm based on the Background subtraction method and the Adaptive Kalman Filtering. Also it proposes the methods of image average processing and the sharpening processing to video in advance, so as to improve tracking accuracy and reduce loss of target. We recommend the way of setting the velocity threshold value because of the slow velocity change of the fish movement. The simulation results show that the tracking accuracy can be greatly improved.



Standards related to Video Tracking

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No standards are currently tagged "Video Tracking"


Jobs related to Video Tracking

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