Transcript Document
Activity Recognition Aneeq Zia 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 Typical methods used for action recognition Evaluation of local spatio-temporal features for action recognition Result Action Recognition by Dense Trajectories 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 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 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 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 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 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 Results Summary • Action recognition using • HMM’s • Temporal Template Matching • Spatio Temporal Interest Points • Bag of Visual Words Technique for action recognition • Dense Trajectories 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”