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Chaotic Invariants for Human Action Recognition

Saad Ali, Arslan Basharat, Mubarak Shah
2007 2007 IEEE 11th International Conference on Computer Vision  
Our contributions in this paper include :1) investigation of the appropriateness of theory of chaotic systems for human action modelling and recognition, 2) a new set of features to characterize nonlinear  ...  The paper introduces an action recognition framework that uses concepts from the theory of chaotic systems to model and analyze nonlinear dynamics of human actions.  ...  The proposed framework for action recognition is built around these basic steps.  ... 
doi:10.1109/iccv.2007.4409046 dblp:conf/iccv/AliBS07 fatcat:bxzsz7n6wzemth3kaa6zo53x5q

Shape Distributions of Nonlinear Dynamical Systems for Video-Based Inference

Vinay Venkataraman, Pavan Turaga
2016 IEEE Transactions on Pattern Analysis and Machine Intelligence  
We provide experimental validation through action and gesture recognition experiments on motion capture and Kinect datasets.  ...  The specific applications of interest in this paper are: 1) activity recognition using motion capture and RGBD sensors, 2) activity quality assessment for applications in stroke rehabilitation, and 3)  ...  chaotic invariants.  ... 
doi:10.1109/tpami.2016.2533388 pmid:27824585 fatcat:pxqg3svrrfhetnk2kh4ynaus7a

Action recognition in videos acquired by a moving camera using motion decomposition of Lagrangian particle trajectories

Shandong Wu, Omar Oreifej, Mubarak Shah
2011 2011 International Conference on Computer Vision  
Recognition of human actions in a video acquired by a moving camera typically requires standard preprocessing steps such as motion compensation, moving object detection and object tracking.  ...  Therefore, action recognition from a moving camera is considered very challenging.  ...  Such conditions are particularly challenging for articulated human action recognition.  ... 
doi:10.1109/iccv.2011.6126397 dblp:conf/iccv/WuOS11 fatcat:r7cuphjlfra7boawwz53wbk2ou

Moving vistas: Exploiting motion for describing scenes

Nitesh Shroff, Pavan Turaga, Rama Chellappa
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
Scene recognition in an unconstrained setting is an open and challenging problem with wide applications. In this paper, we study the role of scene dynamics for improved representation of scenes.  ...  Motivated by these factors, we propose using the theory of chaotic systems to capture dynamics. Due to the lack of a suitable dataset, we compiled a dataset of 'inthe-wild' dynamic scenes.  ...  This framework has been applied to gait modeling by Perc [21] and human action recognition by Ali et al. [1] .  ... 
doi:10.1109/cvpr.2010.5539864 dblp:conf/cvpr/ShroffTC10 fatcat:z7y5gqpnnvcs3ls5oirjojuerq

Deep ChaosNet for Action Recognition in Videos

Huafeng Chen, Maosheng Zhang, Zhengming Gao, Yunhong Zhao, Zhouchao Wei
2021 Complexity  
In this paper, we improve ChaosNet to the deep neural network and apply it to action recognition. First, we extend ChaosNet to deep ChaosNet for extracting action features.  ...  Current methods of chaos-based action recognition in videos are limited to the artificial feature causing the low recognition accuracy.  ...  [3] introduced a human action recognition architecture by using the theory of chaotic systems to model and analyze nonlinear dynamics of human actions.  ... 
doi:10.1155/2021/6634156 fatcat:rhnhy3wornbpdlyvv24657ajra

Human Movement Recognition in Dancesport Video Images Based on Chaotic System Equations

Yongtai Sun, Jingdong Chen, Miaochao Chen
2021 Advances in Mathematical Physics  
This paper presents an in-depth study and analysis of human action recognition in dancesport video images through chaotic system equations.  ...  Finally, circuit simulation experiments and hardware circuit experiments are conducted for the fractional-order chaotic system, and the results are consistent with the corresponding theoretical analysis  ...  Chaotic System Equations for Human Action Recognition Analysis in Dancesport Video Images Chaotic System Equation Design .  ... 
doi:10.1155/2021/5636278 fatcat:37cgbvv36fby3doqaszqju2c6e

PIC: Permutation Invariant Convolution for Recognizing Long-range Activities [article]

Noureldien Hussein, Efstratios Gavves, Arnold W.M. Smeulders
2020 arXiv   pre-print
Neural operations as convolutions, self-attention, and vector aggregation are the go-to choices for recognizing short-range actions.  ...  Unlike standard convolution, PIC is invariant to the temporal permutations of features within its receptive field, qualifying it to model the weak temporal structures. ii.  ...  For example, actions is sports, as UCF [14] , Sports-1M [15] , or human interactions as Kinetics [16] .  ... 
arXiv:2003.08275v1 fatcat:iywbdhu7drhthl3m6rdxbyvpo4

Persistent Homology of Attractors For Action Recognition [article]

Vinay Venkataraman, Karthikeyan Natesan Ramamurthy, Pavan Turaga
2016 arXiv   pre-print
In this paper, we propose a novel framework for dynamical analysis of human actions from 3D motion capture data using topological data analysis.  ...  We model human actions using the topological features of the attractor of the dynamical system.  ...  The mean recognition rates for the different methods are given in Table 1 . Traditional chaotic invariants (Chaos) only achieves a mean recognition rate of 52.44%.  ... 
arXiv:1603.05310v1 fatcat:jiqfxspzancx7m3rqd7nvu4xnu

Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes

Shandong Wu, Brian E. Moore, Mubarak Shah
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
Second, chaotic dynamics are introduced into the crowd context to characterize complicated crowd motions by regulating a set of chaotic invariant features, which are reliably computed and used for detecting  ...  Next, the chaotic dynamics of all representative trajectories are extracted and quantified using chaotic invariants known as maximal Lyapunov exponent and correlation dimension.  ...  This is similar to the work of [7] , where chaotic invariants are used for human action recognition, but a key difference is that we do not use the correlation sum, since we find that F is sufficient  ... 
doi:10.1109/cvpr.2010.5539882 dblp:conf/cvpr/WuMS10 fatcat:cfi4pxwu7rfj5otawuwqizlxya

Attractor-Shape for Dynamical Analysis of Human Movement: Applications in Stroke Rehabilitation and Action Recognition

Vinay Venkataraman, Pavan Turaga, Nicole Lehrer, Michael Baran, Thanassis Rikakis, Steven L. Wolf
2013 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops  
Our experimental results reflect improved action recognition results on two publicly available 3D human activity databases.  ...  In addition, we demonstrate that the proposed framework is sufficiently general for the application of action and gesture recognition as well.  ...  Chaotic invariants, like largest Lyapunov exponent have been extensively used to model human actions [3, 9, 19, 25] .  ... 
doi:10.1109/cvprw.2013.82 dblp:conf/cvpr/VenkataramanTLBRW13 fatcat:o4wsuw5iyrc6fdpuk3reee2bhi

Exploiting Chaotic Feature Vector for Dynamic Textures Recognition

2014 KSII Transactions on Internet and Information Systems  
Finally we investigate recognition rate between different combinations of chaotic features. Experimental results show the merit of chaotic feature vector for pixel intensity series representation.  ...  This paper investigates the description ability of chaotic feature vector to dynamic textures. First a chaotic feature and other features are calculated from each pixel intensity series.  ...  Doretto for sharing the datasets that were used in this paper.  ... 
doi:10.3837/tiis.2014.11.027 fatcat:qspf65csazevfohos6x3ecf6uy

Motion Trend Patterns for Action Modelling and Recognition [chapter]

Thanh Phuong Nguyen, Antoine Manzanera, Matthieu Garrigues
2013 Lecture Notes in Computer Science  
A new method for action modelling is proposed, which combines the trajectory beam obtained by semi-dense point tracking and a local binary trend description inspired from the Local Binary Patterns (LBP  ...  An encoding scheme is proposed and compared with the state-of-the-art through an evaluation performed on two academic action video datasets.  ...  Due to its nice properties in terms of contrast invariance and computation time, LBP is very attractive for many applications, including action recognition.  ... 
doi:10.1007/978-3-642-40261-6_43 fatcat:pbq6vymmq5eytdf4emjvge6iqi


2002 International Journal of Neuroscience  
If all the subsystems are perfectly identical, then the state of identical chaotic synchronization is a possible attractor for the array.  ...  The behavior of such systems is represented by dynamical attractors, which may be fixed point, limit cycle, or chaotic in nature.  ...  NONLINEAR INTERDEPENDENCE IN HUMAN EEG DATA The recognition of synchronization between chaotic systems in the early 1990s initiated a series of algorithms for the analysis of multivariate time series data  ... 
doi:10.1080/00207450290026193 pmid:12587526 fatcat:dtp32nkmnzcqpd43ys43al4rhq

View and Style-Independent Action Manifolds for Human Activity Recognition [chapter]

Michał Lewandowski, Dimitrios Makris, Jean-Christophe Nebel
2010 Lecture Notes in Computer Science  
We introduce a novel approach to automatically learn intuitive and compact descriptors of human body motions for activity recognition.  ...  Each action descriptor is produced, first, by applying Temporal Laplacian Eigenmaps to view-dependent videos in order to produce a stylistic invariant embedded manifold for each view separately.  ...  The authors would like to thank Lena Gorelick from University of Western Ontario and Richard Souvenir from University of North Carolina at Charlotte for sharing their codes.  ... 
doi:10.1007/978-3-642-15567-3_40 fatcat:xrhzykgjdbhblgj7vfbyqmjvtu

Recent Advancements in Signal Processing and Machine Learning

Gelan Yang, Su-Qun Cao, Yue Wu
2014 Mathematical Problems in Engineering  
skeleton model based dynamic features for walking speed invariant gait recognition" by J.  ...  "Tensorial kernel principal component analysis for action recognition" by C.  ...  Acknowledgments Thanks are due to the authors of the special issue for their contributions and thanks to the reviewers for their valuable comments on the submissions.  ... 
doi:10.1155/2014/549024 fatcat:aonsmhahfnaa3g3kzpvjpjuhau
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