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Action Recognition

Action Recognition. Karthik Prabhakar UCF REU 2008, Week 3 Report June 13, 2008. Proposed Model: Overview. SVM Classification. Results. 1) Input. 2) Motion Descriptor. Similar to Efros et. al. [1]. 3) 3D log-polar Binning. Motivated by Belongie et. al. [2]. Compute histogram.

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Action Recognition

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  1. Action Recognition Karthik Prabhakar UCF REU 2008, Week 3Report June 13, 2008

  2. Proposed Model: Overview SVM Classification

  3. Results [1] Recognizing Action at a Distance. A.A. Efros, A.C. Berg, G. Mori, J. Malik. ICCV 2003.

  4. 1) Input

  5. 2) Motion Descriptor • Similar to Efroset. al. [1] [1] Recognizing Action at a Distance. A.A. Efros, A.C. Berg, G. Mori, J. Malik. ICCV 2003.

  6. 3) 3D log-polar Binning • Motivated by Belongieet. al. [2] Compute histogram Count = 4 Count = 10 [2] Shape Matching and Object Recognition Using Shape Contexts. S. Belongie, J. Malik, J. Puzicha. PAMI 2002.

  7. 3) 3D log-polar Binning • We are not dealing with shapes…but with actions (3D not 2D) • Next, we are not concerned with shape…but rather motion!

  8. 3) 3D log-polar Binning * = (a) Fx+ channel (c) Generated Histogram (b) log-polar bins

  9. 4) SVM Classification • Generating feature vectors: • For each video sequence Vi • We get four histograms, H1, H2, H3, H4 of size nsamples x (n_theta * n_r) • These four histograms form the feature vectors for the video sequence • Matching • For now, we are using SVMs to discriminate between different classes of actions.

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