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» Unsupervised Learning of Invariant Features Using Video
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CVPR
2006
IEEE
14 years 9 months ago
Recursive estimation of generative models of video
In this paper we present a generative model and learning procedure for unsupervised video clustering into scenes. The work addresses two important problems: realistic modeling of ...
Nemanja Petrovic, Aleksandar Ivanovic, Nebojsa Joj...
CVPR
2010
IEEE
14 years 3 months ago
Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes
A novel method for crowd flow modeling and anomaly detection is proposed for both coherent and incoherent scenes. The novelty is revealed in three aspects. First, it is a unique ut...
Shandong Wu, Brian E. Moore, and Mubarak Shah
VMV
2001
145views Visualization» more  VMV 2001»
13 years 9 months ago
Registering Real-Scene to Virtual Imagery Using Robust Image Features
The ability to locate objects in a real-time video and relate them to virtual objects in a database is important in a number of applications including visually-guided robotic navi...
Yi Lu Murphey, Jianxin Zhang, Michael DelRose
ICML
2009
IEEE
14 years 8 months ago
Deep learning from temporal coherence in video
This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled v...
Hossein Mobahi, Ronan Collobert, Jason Weston
ICIP
2007
IEEE
14 years 1 months ago
Group Activity Recognition Based on ARMA Shape Sequence Modeling
In this paper, we propose a system identification approach for group activity recognition in traffic surveillance. Statistical shape theory is used to extract features, and then...
Ying Wang, Kaiqi Huang, Tieniu Tan