Video understanding using part based object detection models Video database Part based model Optical flow temporal filter Conditions to ensure temporal coherence Model after iteration 1 Model trained on Imagenet Objects extracted with high confidence Retrain with imagenet + extracted objects Iteration 1 Iteration 2 Dataset bias* * A. Torralba and A. Efros. Unbiased look at dataset bias. In CVPR11, 2011. Vignesh Ramanathan and Kevin Tang (mentor), Stanford University 1

Video understanding using part based object detection models Two event classes and one object for each class. Feature: detection scores of best 10-frame object sequence concatenated for two objects to form 20 element vector. 50 videos, 25 from each class for testing. Limitation: almost 1-day for one iteration of one object ! Vignesh Ramanathan and Kevin Tang (mentor), Stanford University 2