transcript
- 1. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking 2015 / 7 / 26 (Fri.) :
@hokkun_cv GMMCP-Tracker: Globally Optimal Generalized Maximum
Multi Clique Problem for Multiple Object Tracking 1 Afshin Dehghan,
Shayan Modiri Assari, Mubarak Shah University of Central
Florida
- 2. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking About me M2 2014/5CVCNN
2 Preferred Networks @tabe2314
- 3. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Multiple Object
Tracking (MOT) YouTube (GMMCP) 3
- 4. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Multiple Object
Tracking CVPR2015 Target Identity-aware Network Flow for Online
Multiple Target Tracking (Ph.D2 4
- 5. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking H. Possegger et al., In
Defense of Color-based Model-free Tracking detection based) T. Liu
et al., Real-time part-based visual tracking via adaptive
correlation lters S. Tang et al., Subgraph Decomposition for Multi-
Target Tracking 5
- 6. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Data Association
(Navest) 6 Frame n Frame n+1 Bipartite Matching Problem
- 7. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Tracking 7 Detection
Data Association
http://crcv.ucf.edu/projects/GMMCP-Tracker/CVPR15_GMMCP_Presentation.pptx
- 8. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Tracking 8 Detection
Data Association
http://crcv.ucf.edu/projects/GMMCP-Tracker/CVPR15_GMMCP_Presentation.pptx
- 9. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Data Association
(Navest) 9 Frame n Frame n+1 Bipartite Matching Problem
- 10. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Data Association
(Navest) 10 Frame n Frame n+1 Bipartite Matching Problem
- 11. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Data Association
(Network Flow) 11 Frame n Frame n+1 Frame n+2 Frame n+3 sources
sinks minimum-cost maximum-ow problem incorporating motion feature
multi-commodity network
- 12. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking 12 Frame1 Frame2
Frame3
- 13. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking However, Data
association with network ow is simplied formulation of this problem
Assuming no simplication is closer to the tracking scenario in real
world. 13
- 14. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Data Association (Not
Simplify) 14 Frame n Frame n+1 Frame n+2 Frame n+3 =0.95 =0.10
- 15. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Preliminary: clique ()
2 see wikipedia in detail 1 OK 15
- 16. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Data Association (Not
Simplify) 16 Frame n Frame n+1 Frame n+2 Frame n+3 Input: k-partite
complete graph (k) A person form a clique maximum clique
problem
- 17. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking GMCP Tracker[1] The
same team s ECCV 2012 paper They formulate MOT as generalized
maximum clique problem. (cf. former page) 17[1] Amir Roshan Zamir
et al., GMCP-Tracker: Global Multi-object Tracking Using
Generalized Minimum Clique Graphs, ECCV, 2012.
- 18. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking However (2), Due to
complexity of the model, these approaches have been solved by
approximate solutions. GMCP Tracker also used a greedy local
neighborhood search, which is prone to local minima. GMCP Tracker
doesn t follow a joint optimization for all the tracks
simultaneously (one by one). 18
- 19. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Contribution 1. this
approach doesn t involve any simplication neither in formulation
nor in optimization (Binary Integer Problem). 2. they propose a
more ecient occlusion handling strategy, which can handle long-term
occlusions (e.g. 150 frames) and can speed-up the whole algorithm.
19
- 20. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Contribution 1. this
approach doesn t involve any simplication neither in formulation
nor in optimization (Binary Integer Problem). 2. they propose a
more ecient occlusion handling strategy, which can handle long-term
occlusions (e.g. 150 frames) and can speed-up the whole algorithm.
20
- 21. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking 21 Low-level Tracklets
Segment 01 Segment 05 Segment 06 Segment 10 Mid-level Tracklets
Final Trajectories GMMCP GMMCP Input Video Human Detection Detected
Humans
- 22. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking 22 Low-level Tracklets
Segment 01 Segment 05 Segment 06 Segment 10 Mid-level Tracklets
Final Trajectories GMMCP GMMCP Input Video Human Detection Detected
Humans
- 23. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Step 0: Low-level
Tracklet In GMCP, the nodes at rst step are each detections. 23
Frames1-10 In GMMCP, the nodes are (low-level) tracklet How to nd:
bounding boxes that overlap more than 60% between two frames are
regarded as being connected.
- 24. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking 24 Low-level Tracklets
Segment 01 Segment 05 Segment 06 Segment 10 Mid-level Tracklets
Final Trajectories GMMCP GMMCP Input Video Human Detection Detected
Humans
- 25. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Step 1: Mid-level
Tracklet 25 Frames1-10 Frames11-20 Frames21-30 Frames31-40
Frames41-50 Frames51-60
- 26. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Step 1: Mid-level
Tracklet 26 = () + () Frames1-10 Frames11-20 Frames21-30
Frames31-40 Frames41-50 Frames51-60
- 27. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Step 1: Mid-level
Tracklet 27 Frames1-10 Frames11-20 Frames21-30 Frames31-40
Frames41-50 Frames51-60
- 28. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking 28 Low-level Tracklets
Segment 01 Segment 05 Segment 06 Segment 10 Mid-level Tracklets
Final Trajectories GMMCP GMMCP Input Video Human Detection Detected
Humans
- 29. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Step 2: Final
Trajectories The another but similar problem with step 1. They
solve GMMCP: Nodes are Mid-level Tracklet For appearance feature,
they use median (or average) feature among detections in each frame
For motion feature, they use middle point of mid-level tracklet as
the location of each node 29
- 30. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Appearance Anity
Feature: Invariant Color Histogram [2] Deformation and viewpoint
invariant Anity: Histogram Intersection 30[1] J. Domke et al.,
Deformation and Viewpoint Invariant Color Histogram, BMVC, 2006
min(H1[i], H2[i])
- 31. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Motion Anity 31[1] J.
Domke et al., Deformation and Viewpoint Invariant Color Histogram,
BMVC, 2006
- 32. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Optimization GMMCP is
NP Hard, but they solve without any simplication. They formulate
GMMCP as Binary Integer Problem (BIP, 0-1) 32
- 33. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking
33http://www.dais.is.tohoku.ac.jp/
shioura/teaching/dais08/dais02.pdf
- 34. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking
34http://www.dais.is.tohoku.ac.jp/
shioura/teaching/dais08/dais02.pdf
- 35. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking Optimization GMMCP is
NP Hard, but they solve without any simplication. They formulate
GMMCP as Binary Integer Problem (BIP, 0-1) 35 cf. 0-1
- 36. GMMCP-Tracker: Globally Optimal Generalized Maximum Multi
Clique Problem for Multiple Object Tracking BIP in this case C is
weight matrix (?) x is boolean column vector the elements of x is
all of edges and nodes Ax = b is equality constraints Mx