aunifiedframeworkformul45 targettrackingandcollec4ve...
TRANSCRIPT
![Page 1: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/1.jpg)
A Unified Framework for Mul4-‐Target Tracking and Collec4ve
Ac4vity Recogni4on
Wong Choi and Silvio Savarese Presented by: David J. Garcia
November 9, 2012
![Page 2: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/2.jpg)
Organiza4on
• Introduc4on and Problem Descrip4on • Contribu4ons • Technical Details • Experiments • Conclusions and Extensions
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] hWp://www-‐personal.umich.edu/~wgchoi/eccv12/wongun_eccv12.html
![Page 3: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/3.jpg)
Introduc4on and Problem Descrip4on
-‐ Consider the following scenes. What labels would you aWach to the “ac4vi4es” in them?
![Page 4: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/4.jpg)
Introduc4on and Problem Descrip4on
-‐ In this image, perhaps the people are “standing in a line?”
![Page 5: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/5.jpg)
Introduc4on and Problem Descrip4on
-‐ Maybe this one is a talking scene?
![Page 6: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/6.jpg)
Introduc4on and Problem Descrip4on
-‐ What about these two? They seem similar in some ways (a group of people focused inward).
-‐ Maybe one group is converging, and maybe the other is simply wai4ng? It’s difficult to tell without more informa4on. Video might be able to add addi4onal informa4on.
![Page 7: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/7.jpg)
Introduc4on and Problem Descrip4on -‐ To infer an ac4vity in a video scene…track subjects and infer
pairwise ac4vi4es.
-‐ Problems addressed simultaneously with a hierarchical graphical model.
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] hWp://www-‐personal.umich.edu/~wgchoi/eccv12/wongun_eccv12.html
![Page 8: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/8.jpg)
Contribu4ons -‐ Graphical model that correlates collec4ve ac4vity, pairwise
ac4vity and individual ac4vity in a hierarchical fashion. -‐ Simultaneously solving the tracking problem and the ac4vity
inference problem: The tracking problem is solved with help from annotated data.
-‐ Solving this “joint inference problem” with a novel algorithm that combines belief propaga4on and the branch and bound algorithm.
-‐ The authors also evaluate their method against challenging datasets.
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] hWp://www-‐personal.umich.edu/~wgchoi/eccv12/wongun_eccv12.html
![Page 9: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/9.jpg)
Technical Details: Ac4vity Modeling
Collec4ve-‐Ac4vity: C (Oc is called the crowd context descriptor) Pairwise-‐Ac4vity: I (i and j are the individual subjects in the interac4on) Individual-‐Ac4vity: A (Oi are the “appearance features” of subject i) These include features like HoG [3] and BoV [4]
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] hWp://www-‐personal.umich.edu/~wgchoi/eccv12/wongun_eccv12.html -‐ [3] Dalal, N., Triggs, B.: Histograms of oriented gradients for human detec4on. CVPR. 2005 -‐ [4] Dollar, P., Rabaud, V., CoWrell, G., Belongie, S. Behavior recogni4on via sparse spa4o-‐temporal features. VS-‐PETS. 2005
![Page 10: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/10.jpg)
Technical Details: Collec4ve Observa4ons…the STL descriptor
• The spa4o-‐temporal local (STL) descriptor is a binning-‐style descriptor.
• An “anchor” is chosen (indicated in blue). • The loca4on (and pose) of every other
person is noted and placed into a bin rela4ve to the anchor.
• These histograms are “stacked” along the 4me dimension..
• For more informa4on, please see [3] and [4]
• Side Note: Almost like “leWer” recogni4on? -‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] hWp://www-‐personal.umich.edu/~wgchoi/eccv12/wongun_eccv12.html -‐ [3] W. Choi, S. K. Savarese. What are they doing?: Collec4ve ac4vity classifica4on using spa4o-‐temporal
rela4onship among people. VSWS 2009 -‐ [4] W. Choi, S. K. Savarese. Learning context for collec4ve ac4vity recogni4on. CVPR 2011
![Page 11: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/11.jpg)
Technical Details: Tracking Problem -‐ Although it’s assumed that only one collec4ve ac4vity is present in any
one scene(mul4ple frames), mul4ple subjects need to be tracked.
-‐ The tracklet associa4on problem can be understood as a matching problem.
-‐ The best associa4on is the one of LEAST resistance…
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on
![Page 12: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/12.jpg)
Technical Details: Combining Everything
-‐ This track associa4on is not good if this is a crossing ac4vity.
-‐ [1]W. Choi, S. Savarese. Slides: hWp://www.umich.edu/~wgchoi/eccv12/choi_eccv12_final_web.pptx -‐ [2] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on
𝛹(𝐶, 𝐼, 𝐴, 𝑂, 𝑓) = 𝛹(𝐴, 𝑂)+𝛹(𝐼, 𝐴, 𝑓) + 𝛹(𝐶, 𝐼)+𝛹(𝐶, 𝑂) +
𝛹(𝐶)+𝛹(𝐼)+𝛹(𝐴)− 𝑐↑𝑇 𝑓, 𝑓∈𝑺
Tracking
Crossing
![Page 13: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/13.jpg)
Technical Details: Combining Everything
-‐ This is a much beWer associa4on.
-‐ [1]W. Choi, S. Savarese. Slides: hWp://www.umich.edu/~wgchoi/eccv12/choi_eccv12_final_web.pptx -‐ [2] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on
𝛹(𝐶, 𝐼, 𝐴, 𝑂, 𝑓) = 𝛹(𝐴, 𝑂)+𝛹(𝐼, 𝐴, 𝑓) + 𝛹(𝐶, 𝐼)+𝛹(𝐶, 𝑂) +
𝛹(𝐶)+𝛹(𝐼)+𝛹(𝐴)− 𝑐↑𝑇 𝑓, 𝑓∈𝑺
Tracking
Crossing
![Page 14: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/14.jpg)
Technical Details: Classifica4on + ? + Tracking = Profit !!!
-‐ The two problems combined as a maximiza4on of the following:
-‐ Sec4on 3 dealt with the classifica4on bit.
-‐ Sec4on 4 defined the tracking problem.
-‐ Note that ‘f’ is incorporated in both terms.
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on
![Page 15: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/15.jpg)
Technical Details: Divide and Conquer -‐ The compact equa4on on the previous slide is broken up into two problems and
solved itera4vely.
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] Choi, W., Savarese, S.: Supplementary material. In: ECCV. (2012)
Hold f constant, and solve via Belief Propaga4on. Obtain ‘I’ and ‘A’
With ‘I’ and ‘A’, find f with Branch-‐and-‐Bound algorithm[2].
![Page 16: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/16.jpg)
Experiments -‐ Video-‐words are obtained by applying PCA (principal component analysis)
with 200 dimensions and using K-‐means with 100 code words on the cuboids described in [2].
-‐ Collec4ve ac4vity features are computed using STL on tracklets[3].
-‐ Presumably, tracklets are obtained with [4].
-‐ STL set for 8 meters and 60 frames for reinforcement.
-‐ Experiments are done over two datasets.
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] Dollar, P., Rabaud, V., CoWrell, G., Belongie, S. Behavior recogni4on via sparse spa4o-‐temporal features. VS-‐
PETS. 2005 -‐ [3] W. Choi, S. K. Savarese. What are they doing?: Collec4ve ac4vity classifica4on using spa4o-‐temporal
rela4onship among people. VSWS 2009 -‐ [4] Choi, W., Savarese, S.: Mul4ple target tracking in world coordinate with single, minimally calibrated camera. In:
ECCV. (2010)
![Page 17: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/17.jpg)
Experiments: Dataset One -‐ First dataset is composed of 44 video clips with annota4ons for 5 collec4ve
ac4vi4es (crossing, wai4ng, queuing, walking, and talking) and 8 poses (right, right-‐front,…, right back). 8 types of interac4ons are annotated:
-‐ AP (approaching) -‐ LV (leaving) -‐ PB (passing-‐by) -‐ FE (facing-‐each-‐other) -‐ WS (walking-‐side-‐by-‐side) -‐ SR (standing-‐in-‐a-‐row) -‐ SS (standing-‐side-‐by-‐side) -‐ NA (no interac4ons)
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] ]W. Choi, S. Savarese. Slides: hWp://www.umich.edu/~wgchoi/eccv12/choi_eccv12_final_web.pptx
Crossing Wai4ng Queuing
Walking Talking
![Page 18: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/18.jpg)
Experiments: Dataset Two -‐ Second dataset is composed of 32 video clips with annota4ons for 6 collec4ve ac4vi4es
(gathering, talking, dismissal, walking, together, chasing, queueing) and 8 poses (right, right-‐front,…, right back). 9 types of interac4ons are annotated:
-‐ AP (approaching) -‐ WO (walking-‐in-‐opposite-‐direc4on) -‐ WR (walking-‐one-‐ater-‐the-‐other) -‐ RS (running-‐side-‐by-‐side) -‐ RR (running-‐one-‐ater-‐the-‐other) -‐ FE (facing-‐each-‐other) -‐ WS (walking-‐side-‐by-‐side) -‐ SR (standing-‐in-‐a-‐row) -‐ NA (no interac4ons)
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] ]W. Choi, S. Savarese. Slides: hWp://www.umich.edu/~wgchoi/eccv12/choi_eccv12_final_web.pptx
Gathering Talking Dismissal
Walking-‐together
Chasing Queuing
![Page 19: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/19.jpg)
Experiments: Dataset One for Classifica4on -‐ Accuracy measure show significant improvement for the method
-‐ The numbers on top are mean-‐accuracy per class, and overall accuracy. -‐ The basic SVM isn’t terrible and much simpler to do.
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on
![Page 20: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/20.jpg)
Experiments: Dataset Two for Classifica4on -‐ Dataset two also shows improvement over the baseline
-‐ The numbers on top are mean-‐accuracy per class, and overall accuracy. -‐ What do the “Gathering” and “Dissmissal” ac4vi4es have in common?
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on
Gathering
Dismissal
![Page 21: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/21.jpg)
Experiments: Graphical Model Experiments -‐ Is the graphical model correct? The authors test different versions:
-‐ The authors cut some of these links and see what happens to classifica4on accuracy.
-‐ The Collec4ve observa4ons are clearly the most important.
-‐ Baseline: SVM over Collec4ve ac4vity observa4ons (i.e. STL[2]). -‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] W. Choi, S. K. Savarese. What are they doing?: Collec4ve ac4vity classifica4on using spa4o-‐temporal rela4onship among people. VSWS
2009
![Page 22: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/22.jpg)
Experiments: Temporal Support’s effect on Classifica4on -‐ What is the sensi4vity of and to the temporal support
window? The authors isolate each window and test different sizes.
-‐ Authors claim that classifica4on becomes more robust. Perhaps for collec4ve ac4vity more so than for pairwise-‐inference? How conclusive are these results?
-‐ These values directly affect the “cuboid” based features[2]. Perhaps some windows are beWer for par4cular ac4vi4es.
-‐ [1] W. Choi, S. Savarese. A Unified Framework for Mul4-‐Target Tracking and Collec4ve Ac4vity Recogni4on -‐ [2] Dollar, P., Rabaud, V., CoWrell, G., Belongie, S.: Behavior recogni4on via sparse spa4o-‐temporal features. In: VS-‐PETS. (2005)
![Page 23: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/23.jpg)
Conclusions/Extensions -‐ Graphical model links that seemed most important were Collec4ve-‐
Observa4ons link and the temporal link between collec4ve ac4vi4es.
-‐ Is the s4ll camera assump4on reasonable (maybe for some applica4ons)? This stems from the ‘cuboid’ [1].
-‐ Authors suggest a mul4-‐ac4vity detector as a possible extension.
-‐ Finer grained temporal considera4ons (like different temporal windows for different ac4vi4es)
-‐ [1] Dollar, P., Rabaud, V., CoWrell, G., Belongie, S.: Behavior recogni4on via sparse spa4o-‐temporal features. In: VS-‐PETS. (2005)
![Page 24: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/24.jpg)
Detec4ng Ac4ons, Poses, and Objects with Rela4onal Phraselets
-‐ Person-‐Object “composites” are combined “into local patches or ‘phraselets.’” -‐ Phraselets are then used for learning ater a clustering step. -‐ There are separate “mixtures for visible and occluded parts.”
-‐ [1] C. Desai. D. Ramanan. Detec4ng Ac4ons, Poses, and Objects with Rela4onal Phraselets
![Page 25: AUnifiedFrameworkforMul45 TargetTrackingandCollec4ve ...cv-fall2012/slides/david-paper.pdfA"Unified"Framework"for"Mul45 TargetTracking"and"Collec4ve" Ac4vity"Recogni4on" " Wong"Choi"and"Silvio"Savarese"](https://reader033.vdocument.in/reader033/viewer/2022050605/5facf5613ab3e5474f414127/html5/thumbnails/25.jpg)
Learning realis4c human ac4ons from movies -‐ Movies used to train SVM to detect ac4ons. -‐ Scripts are used to aid in ac4on annota4on. -‐ Spa4al-‐Temporal features are extracted for the
sequences/subsequences. -‐ SVM is trained on clustered BoF data-‐points.
-‐ KTH dataset evalua4on[2].
-‐ [1] I. Laptev, M. Marszalek, C. Schmid, B. Rozenfeld. Learning realis4c human ac4ons from movies -‐ [2] C. Schuldt, I. Laptev, and B. Caputo. Recognizing human ac4ons: A local SVM approach. In ICPR, 2004.