PPT-Online Detection of Unusual Events in Videos via Dynamic Sparse Coding

Outline Unusual Event Detection Video Representation Dynamic Sparse Coding Empirical Study Conclusions Outline Unusual Event Detection Video Representation Dynamic Sparse Coding Empirical Study. We consider graphs of bounded arboricity ie graphs with no dense subgraphs l ike for example planar graphs Brodal and Fagerberg WADS99 described a very simple line arsize data structure which processes queries in constant worstcase time and performs illinoisedu kyusvneclabscom Abstract Sparse coding of sensory data has recently attracted notable attention in research of learning useful features from the unlabeled data Empirical studies show that mapping the data into a signi64257cantly higher di - Israel . Institute of Technology. D. epartment . of Electrical Engineering . Abnormal Event Detection at 150FPS in MATLAB. Cewu Lu · Jianping . SHI · . Jiaya Jia. The Chinese University of hong kong. Raja . Giryes. ICASSP 2011. Volkan. Cevher. Agenda. The sparse approximation problem. Algorithms and pre-run guarantees. Online performance guarantees. Performance bound. Parameter selection. 2. Sparse approximation. Ph.D. Thesis Defense. Anoop Cherian. *. Department of Computer Science and Engineering. University of Minnesota, Twin-Cities. Adviser. : Prof. Nikolaos Papanikolopoulos. *Contact: . cherian@cs.umn.edu. onto convex sets. Volkan. Cevher. Laboratory. for Information . . and Inference Systems – . LIONS / EPFL. http://lions.epfl.ch . . joint work with . Stephen Becker. Anastasios. . Kyrillidis. ISMP’12. KH Wong. mean transform v.5a. 1. Introduction. What is object tracking. Track an object in a video, the user gives an initial bounding box. Find the bounding box that cover the target pattern in every frame of the video. Identify new or changing . risks . and take appropriate actions through:. Early . detection . of. Novel pathogens. Variations of old . pathogens . Understand trends and patterns of disease. To inform public health policy: who should be targeted, what interventions are useful, etc.. Author: . Vikas. . Sindhwani. and . Amol. . Ghoting. Presenter: . Jinze. Li. Problem Introduction. we are given a collection of N data points or signals in a high-dimensional space R. D. : xi ∈ . Object Recognition. Murad Megjhani. MATH : 6397. 1. Agenda. Sparse Coding. Dictionary Learning. Problem Formulation (Kernel). Results and Discussions. 2. Motivation. Given a 16x16(or . nxn. ) image . Parallelization of Sparse Coding & Dictionary Learning Univeristy of Colorado Denver Parallel Distributed System Fall 2016 Huynh Manh 11/15/2016 1 Contents Introduction to Sparse Coding Applications of Sparse Representation Vimal Singh, . Ahmed H. Tewfik. The University of Texas at Austin. 1. Outline. Introduction. Algorithm. Results. Conclusions. 2. Introduction. Algorithm. Results. Conclusions. Significance. Fast magnetic resonance . By: . Ayan. . Batyrkhanov. For Horizon-t group. 1. Horizon-T Group. R. .. U. . . Beisembaev. , . E. .. A. . . Beisembaeva. , . O. .. D. . . Dalkarov. , . V. .. A. . . Ryabov. , . S. .. B. . . Shaulov. Le Yang, Junwei Han, Dingwen Zhang. School of Automation, Northwestern Polytechnical University, China. Motivation. Existing works strive to learn . a coherent representation. for all frames belonging to the same category..