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Action Recognition

Action Recognition. Karthik Prabhakar UCF REU 2008, Week 1 Report May 23, 2008. About Me. Purdue University – Indianapolis Computer Science ( Junior Senior) Non-Academic Interests: Cricket. Major Research Projects.

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Action Recognition

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  1. Action Recognition Karthik Prabhakar UCF REU 2008, Week 1 Report May 23, 2008

  2. About Me • Purdue University – Indianapolis • Computer Science (Junior Senior) • Non-Academic Interests: • Cricket

  3. Major Research Projects • “Identifying and Extracting Biological Entity Relationships From Textual Documents Using Support Vector Machines” • March 2006 – July 2007 • Developed a system to extract biological relationships (gene-gene, protein-protein, etc.) in free-flowing biomedical text abstracts. • “Real-Time Mental Workload Evaluation Using Multi-Modal Biometric Data” • August 2007 – January 2008 • Designed a real-time eye tracking software for the integrated eye tracking device. • Designed and implemented a system to evaluate mental workload based on eye characteristics (movement, pupil dilation) and facial thermal images

  4. Individual ‘Research’ Projects • Face Recognition Using PCA and LDA • Yale Face Database B (15 subjects, 11 images per) • PCA  SVM + LDA  SVM • Optimal: 2 training per = 68% accuracy on rank 1 • Multimodal Biometric System Using Face and Iris Recognition • Combined face recognition from above with iris recognition • Combined classifier outputs from both. • Optimal: 2 training face, 1 training iris = 78% accuracy on rank 1 • Object Recognition Using Local and Global Features • Again, combination at classifier outputs. • Caltech101 database. • 12 classes, 50 images per (20 training, 30 testing). Accuracy = 31%

  5. Future Plans • Goals and expectations for the summer: • Experience research at another institution. • Contribute positively to the program. • Publish a paper. • Long-term plans: • Graduate School (PhD in Computer Science) • Become a professor….hopefully!

  6. Edge Detection (Video) Canny Filter s = 1.6, L = 0.5, H = 0.8 Original Video Default Canny Filter

  7. Edge Detection (People Count)

  8. Segmentation • Segmentation results using k-means: Original K-means (color) K-means (color + dist)

  9. Segmentation (contd.) Original K-means (color) K-means (color + dist)

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