Journal ArticleDOI
Motion history image: its variants and applications
Md. Atiqur Rahman Ahad,Joo Kooi Tan,Hyoungseop Kim,Seiji Ishikawa +3 more
- Vol. 23, Iss: 2, pp 255-281
TLDR
This paper provides an overview of MHI-based human motion recognition techniques and applications and points some areas for further research based on the MHI method and its variants.Abstract:
The motion history image (MHI) approach is a view-based temporal template method which is simple but robust in representing movements and is widely employed by various research groups for action recognition, motion analysis and other related applications. In this paper, we provide an overview of MHI-based human motion recognition techniques and applications. Since the inception of the MHI template for motion representation, various approaches have been adopted to improve this basic MHI technique. We present all important variants of the MHI method. This paper points some areas for further research based on the MHI method and its variants.read more
Citations
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Journal ArticleDOI
Event-based Vision: A Survey
Guillermo Gallego,Tobi Delbruck,Garrick Orchard,Chiara Bartolozzi,Brian Taba,Andrea Censi,Stefan Leutenegger,Andrew J. Davison,Jörg Conradt,Kostas Daniilidis,Davide Scaramuzza +10 more
TL;DR: This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras.
Journal ArticleDOI
Going deeper into action recognition
TL;DR: This survey provides a comprehensive review of the notable steps taken towards recognizing human actions, starting with the pioneering methods that use handcrafted representations, and then, navigating into the realm of deep learning based approaches.
Proceedings Article
Highly accurate optic flow computation with theoretically justified warping
TL;DR: In this article, a variational model for optic flow computation based on non-linearised and higher order constancy assumptions is proposed, which is also capable of dealing with large displacements.
Posted Content
Going Deeper into Action Recognition: A Survey
TL;DR: A comprehensive review of the notable steps taken towards recognizing human actions can be found in this article, where the authors start with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches.
Journal ArticleDOI
A survey of human motion analysis using depth imagery
TL;DR: This survey starts by explaining the advantages of depth imagery, then describes the new sensors that are available to obtain it, and describes the software libraries that can acquire it from a sensor.
References
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Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.
Journal ArticleDOI
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
TL;DR: New results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form that provide the basis for an automatic system that can solve the Location Determination Problem under difficult viewing.
Proceedings Article
An iterative image registration technique with an application to stereo vision
Bruce D. Lucas,Takeo Kanade +1 more
TL;DR: In this paper, the spatial intensity gradient of the images is used to find a good match using a type of Newton-Raphson iteration, which can be generalized to handle rotation, scaling and shearing.
Journal ArticleDOI
Determining optical flow
TL;DR: In this paper, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.
Proceedings ArticleDOI
Determining Optical Flow
TL;DR: In this article, a method for finding the optical flow pattern is presented which assumes that the apparent velocity of the brightness pattern varies smoothly almost everywhere in the image, and an iterative implementation is shown which successfully computes the Optical Flow for a number of synthetic image sequences.