transcript
- Slide 1
- Activity Recognition Aneeq Zia
- Slide 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
- Slide 3
- Slide 4
- Slide 5
- Slide 6
- Typical methods used for action recognition
- Slide 7
- Slide 8
- Slide 9
- Slide 10
- Slide 11
- Evaluation of local spatio- temporal features for action
recognition
- Slide 12
- Slide 13
- Slide 14
- Slide 15
- Slide 16
- Slide 17
- Slide 18
- Slide 19
- Slide 20
- Result
- Slide 21
- Action Recognition by Dense Trajectories
- Slide 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
- Slide 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
- Slide 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
- Slide 25
- Slide 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
- Slide 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
- Slide 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
- Slide 29
- Results
- Slide 30
- Summary Action recognition using HMMs Temporal Template
Matching Spatio Temporal Interest Points Bag of Visual Words
Technique for action recognition Dense Trajectories
- Slide 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