1. Activity Recognition Aneeq Zia

2. Agenda What is activity recognition Typical methods used for action recognition “Evaluation of local spatio-temporal features for action recognition”, Heng Wang et all “Action Recognition by Dense Trajectories”, Heng Wang et all Summary References

6. Typical methods used for action recognition

11. Evaluation of local spatio- temporal features for action recognition

20. Result

21. Action Recognition by Dense Trajectories

22. Dense Trajectories Feature trajectories have shown to be efficient for representing videos Extracted using KLT tracker or matching SIFT descriptors between frames However, the quantity and quality is generally not enough This paper proposes an approach to describe videos by dense trajectories

23. Dense Trajectories The trajectories are obtained by tracking densely sampled points using optical flow fields A local descriptor is introduced that overcomes the problem of camera motion The descriptor extends the motion coding scheme based motion motion boundaries developed in the context of human detection

24. Dense Trajectories Feature points are sampled on a grid spaced by W (=5) pixels and tracked in each scale separately 8 spatial scales used Each point in a certain frame is tracked to the next frame using median filtering in a dense optical flow field

26. Tracking Points of subsequent frames are concatenated to form a trajectory Trajectories are limited to ‘L’ frames in order to avoid drift from their initial location The shape of a trajectory of length ‘L’ is described by the sequence where The resulting vector is normalized by

27. Trajectory descriptors Histogram of Oriented Gradient (HOG) Histogram of Optical Flow (HOF) HOGHOF Motion Boundary Histogram (MBH) Take local gradients of x-y flow components and compute HOG as in static images

28. Bag of Features Codebook of descriptors (trajectories, HOG, HOF, MBH) constructed Number of visual words = 4000 100,000 randomly selected training features used Each video described by a histogram of visual word occurances Non-linear SVM with Chi-Square kernel used to classify the actions

29. Results

30. Summary Action recognition using HMM’s Temporal Template Matching Spatio Temporal Interest Points Bag of Visual Words Technique for action recognition Dense Trajectories

31. References “ Evaluation of local spatio-temporal features for action recognition”,Heng Wang et all “Action Recognition by Dense Trajectories”, Heng Wang et all CVPR 2011 tutorial on “Human Activity Analysis” CVPR 2014 tutorial on “Emerging topics in Human Activity recognition”