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Hidden Markov Models for Video Event Annotation in Computer Vision

Learn about Hidden Markov Models and their application in computer vision for event annotations. Quantize data, tabularize based on video, train HMMs, separate models, test accuracy, utilize forward algorithm, and explore training for higher effectiveness.

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Hidden Markov Models for Video Event Annotation in Computer Vision

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Presentation Transcript

  1. Week 4 Presentation Sahil Shah

  2. Learn about HMM • Read papers about how Hidden Markov Models work and their application to computer vision

  3. Format Event Annotations • Given annotations for 3 classes (board trick, flash mob, making a sandwich) • Quantize data – give each type of concept a numerical ID • Tabularize data based on video

  4. Train HMM • Use annotation data to generate the maximum likelihood prior, transition and observation matrices • Separate HMM model for each of the three classes

  5. Test HMM • First test was of each video against each of the three models • 82 correct classifications out of 88 videos (93% precision) • High precision expected because annotations were done manually for preliminary tests

  6. Test HMM #2 • Partition training and testing data using K-Fold for each event (K=5) testing/training Log likelihood Average

  7. Test HMM #3 • Forward algorithm (also known as -pass) • Calculate P(O|𝛌) / N^T

  8. Continuing Work • Look at why the forward probabilities are so low • Look at the effects of varying the number of hidden states • Train this HMM for more annotations to get a better idea of its effectiveness

  9. The end

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