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Detecting Actions, Poses, and Objects with Relational Phraselets
Published on Nov 12, 20125062 Views
We present a novel approach to modeling human pose, together with interacting objects, based on compositional models of local visual interactions and their relations. Skeleton models, while flexible
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Chapter list
Detecting Actions, Poses, and Objects with Relational Phraselets00:00
Chaitanya Desai00:06
K-way action classification - 100:11
K-way action classification - 200:29
What’s wrong with K-way classification? - 100:37
What’s wrong with K-way classification? - 200:45
Our goal: detailed action understanding - 101:03
Our goal: detailed action understanding - 201:15
Our goal: detailed action understanding - 301:20
Our goal: detailed action understanding - 401:24
Challenge 1: human pose estimation01:32
Challenge 2: person-object occlusions01:44
(Revised) action understanding01:53
Related work: PASCAL Action Classification Challenge02:21
Our approach02:42
Articulated pose estimation - 102:53
Articulated pose estimation - 203:16
Articulated pose estimation - 303:39
Visual Phrases - 103:51
Visual Phrases - 204:12
Geometric parts (poselets) - 104:22
Geometric parts (poselets) - 204:53
Approach05:08
Articulated models + visual composites05:13
Articulated models + visual composites + geometric parts05:24
Learning phraselets05:53
Clusters06:20
Model occlusions with separate clusters06:32
Local mixtures of phraselets - 106:52
Local mixtures of phraselets - 207:06
Local mixtures of phraselets - 307:22
Geometry-dependent parts07:46
Inference & Learning08:14
Possible Criticisms - 108:44
Possible Criticisms - 209:00
Possible Criticisms - 309:16
Possible Criticisms - 409:24
Experimental Results09:45
High-confidence detections10:04
High-scoring false-positives10:25
Evaluation10:54
Action classification11:00
Articulated pose estimation11:15
Action detection/ localization11:32
Action detection11:35
A look back - 112:13
A look back - 212:30
Thank you12:56