Week 9 Presented by Christina Peterson. Recognition Accuracies on UCF Sports data set Method Accuracy (%)DivingGolfingKickingLiftingRidingRunningSkating.

Slides:

Advertisements

Similar presentations

Max-Margin Additive Classifiers for Detection

Advertisements

Real-Time Detection, Alignment and Recognition of Human Faces

Adjusting Active Basis Model by Regularized Logistic Regression

A Discriminative Key Pose Sequence Model for Recognizing Human Interactions Arash Vahdat, Bo Gao, Mani Ranjbar, and Greg Mori ICCV2011.

Recognizing Human Actions by Attributes CVPR2011 Jingen Liu, Benjamin Kuipers, Silvio Savarese Dept. of Electrical Engineering and Computer Science University.

EE462 MLCV Lecture 5-6 Object Detection – Boosting Tae-Kyun Kim.

Limin Wang, Yu Qiao, and Xiaoou Tang

Rapid Object Detection using a Boosted Cascade of Simple Features Paul Viola, Michael Jones Conference on Computer Vision and Pattern Recognition 2001.

Human Action Recognition by Learning Bases of Action Attributes and Parts Bangpeng Yao, Xiaoye Jiang, Aditya Khosla, Andy Lai Lin, Leonidas Guibas, and.

Lecture 31: Modern object recognition

Some Recent Works of Human Activity Recognition 吴心筱

EE462 MLCV Lecture 5-6 Object Detection – Boosting Tae-Kyun Kim.

Enhancing Exemplar SVMs using Part Level Transfer Regularization 1.

Detecting Pedestrians by Learning Shapelet Features

Discriminative and generative methods for bags of features

Local Descriptors for Spatio-Temporal Recognition

Robust Moving Object Detection & Categorization using self- improving classifiers Omar Javed, Saad Ali & Mubarak Shah.

Good morning, everyone, thank you for coming to my presentation.

Graz University of Technology, AUSTRIA Institute for Computer Graphics and Vision Fast Visual Object Identification and Categorization Michael Grabner,

Generic Object Detection using Feature Maps Oscar Danielsson Stefan Carlsson

Object Recognition with Informative Features and Linear Classification Authors: Vidal-Naquet & Ullman Presenter: David Bradley.

Region-based Voting Exemplar 1 Query 1 Exemplar 2.

Face detection and recognition Many slides adapted from K. Grauman and D. Lowe.

Viola and Jones Object Detector Ruxandra Paun EE/CS/CNS Presentation

Dynamic Cascades for Face Detection 第三組馮堃齊、莊以暘. 2009/01/072 Outline Introduction Dynamic Cascade Boosting with a Bayesian Stump Experiments Conclusion.

Viewpoint Tracking for 3D Display Systems A look at the system proposed by Yusuf Bediz, Gözde Bozdağı Akar.

Face Detection CSE 576. Face detection State-of-the-art face detection demo (Courtesy Boris Babenko)Boris Babenko.

Real-time Action Recognition by Spatiotemporal Semantic and Structural Forest Tsz-Ho Yu, Tae-Kyun Kim and Roberto Cipolla Machine Intelligence Laboratory,

Face Alignment Using Cascaded Boosted Regression Active Shape Models

Bag of Video-Words Video Representation

Using Statistic-based Boosting Cascade Weilong Yang, Wei Song, Zhigang Qiao, Michael Fang 1.

Marcin Marszałek, Ivan Laptev, Cordelia Schmid Computer Vision and Pattern Recognition, CVPR Actions in Context.

“Secret” of Object Detection Zheng Wu (Summer intern in MSRNE) Sep. 3, 2010 Joint work with Ce Liu (MSRNE) William T. Freeman (MIT) Adam Kalai (MSRNE)

Window-based models for generic object detection Mei-Chen Yeh 04/24/2012.

Lecture 29: Face Detection Revisited CS4670 / 5670: Computer Vision Noah Snavely.

Face detection Slides adapted Grauman & Liebe’s tutorial

Visual Object Recognition

Lecture 31: Modern recognition CS4670 / 5670: Computer Vision Noah Snavely.

Pedestrian Detection and Localization

Semantic Embedding Space for Zero Shot Action Recognition Xun XuTimothy HospedalesShaogang GongAuthors: Computer Vision Group Queen Mary University of.

Chao-Yeh Chen and Kristen Grauman University of Texas at Austin Efficient Activity Detection with Max- Subgraph Search.

Limitations of Cotemporary Classification Algorithms Major limitations of classification algorithms like Adaboost, SVMs, or Naïve Bayes include, Requirement.

Real-Time Detection, Alignment and Recognition of Human Faces Rogerio Schmidt Feris Changbo Hu Matthew Turk Pattern Recognition Project June 12, 2003.

Visual Categorization With Bags of Keypoints Original Authors: G. Csurka, C.R. Dance, L. Fan, J. Willamowski, C. Bray ECCV Workshop on Statistical Learning.

Project 3 Results.

Grouplet: A Structured Image Representation for Recognizing Human and Object Interactions Bangpeng Yao and Li Fei-Fei Computer Science Department, Stanford.

E XEMPLAR -SVM FOR A CTION R ECOGNITION Week 11 Presented by Christina Peterson.

The Viola/Jones Face Detector A “paradigmatic” method for real-time object detection Training is slow, but detection is very fast Key ideas Integral images.

Robust Nighttime Vehicle Detection by Tracking and Grouping Headlights Qi Zou, Haibin Ling, Siwei Luo, Yaping Huang, and Mei Tian.

Notes on HW 1 grading I gave full credit as long as you gave a description, confusion matrix, and working code Many people’s descriptions were quite short.

Face detection and recognition Many slides adapted from K. Grauman and D. Lowe.

Face detection Many slides adapted from P. Viola.

Hierarchical Motion Evolution for Action Recognition Authors: Hongsong Wang, Wei Wang, Liang Wang Center for Research on Intelligent Perception and Computing,

1 Bilinear Classifiers for Visual Recognition Computational Vision Lab. University of California Irvine To be presented in NIPS 2009 Hamed Pirsiavash Deva.

Data Driven Attributes for Action Detection

Action Recognition ECE6504 Xiao Lin.

Exemplar-SVM for Action Recognition

Object detection as supervised classification

Action Recognition in Temporally Untrimmed Videos

Efficient Deformable Template Matching for Face Tracking

Bilinear Classifiers for Visual Recognition

Data Driven Attributes for Action Detection

AHED Automatic Human Emotion Detection

Weakly Supervised Action Recognition

Exemplar-SVM for Action Recognition

University of Central Florida

AHED Automatic Human Emotion Detection

AHED Automatic Human Emotion Detection

Lecture 29: Face Detection Revisited

Presentation transcript:

Week 9 Presented by Christina Peterson

Recognition Accuracies on UCF Sports data set Method Accuracy (%)DivingGolfingKickingLiftingRidingRunningSkating Swing- bench High- swingWalking Rodriguez et al. [1] Yeffet and Wolf [2] Le et al. [4] Wu et al. [6] Action Bank [7] Standard Multiclass-SVMs Combined Exemplar-SVMs

Confusion Matrix: Standard Multi-Class SVM DiGoSsSbSkRuHoLiKiWa Diving Golf Kick Lift Horse-Ride Run Skateboard Swing-bench Swing-side Walk

Confusion Matrix: Combined Exemplar-SVM DiGoSsSbSkRuHoLiKiWa Diving Golf Kick Lift Horse-Ride Run Skateboard Swing-bench Swing-side Walk

Standard Linear vs. Exemplar

Conclusions Need to improve the method used to combine the exemplar scores Currently, a multiclass-svm is trained on the decision values of the exemplars on the validation set Possible Solution: Create a strong action classifier using the boosting algorithm of Viola and Jones[8] Treat the exemplar-svms as weak classifiers

References [1] M. D. Rodriguez, J. Ahmed, and M. Shah. Action mach: A spatio-temporal maximum average correlation height ﬁlter for action recognition. In CVPR, [2] Yeffet and L. Wolf. Local trinary patterns for human action recognition. In ICCV, [3] H. Wang, M. Ullah, A. Klaser, I. Laptev, and C. Schmid. Evaluation of local spatiotemporal features for action recognition. In BMVC, [4] Q. Le, W. Zou, S. Yeung, and A. Ng. Learning hierarchical invariant spatiotemporal features for action recognition with independent subspace analysis. In CVPR, [5] A. Kovashka and K. Grauman. Learning a hierarchy of discriminative spacetime neighborhood features for human action recognition. InCVPR, [6] X. Wu, D. Xu, L. Duan, and J. Luo. Action recognition using context and appearance distribution features. InCVPR, [7] S. Sadanand and J. J. Corso. Action bank: A high-level representation of activity in video. CVPR, [8] P. Viola and M. Jones. Robust real-time face detection. International Journal of Computer Vision, 57(2):137–154, May 2004.