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1
Intro
2
Challenges
3
Multi-target Tracking: Applications
4
Outline
5
Data Association
6
GMCP Tracker: Pipeline
7
How to solve GMCP?
8
Process of Finding Tracklets in one Segment
9
Parking Lot Results
10
Evaluation Metrics
11
Limitations
12
What are the main differences?
13
Framework
14
Mid-level Tracklet Generation
15
Optimization
16
Aggregated Dummy Nodes (ADN)
17
Run-time Comparison
18
Qualitative Results
19
Parking Lot 2
20
Occlusion Handling
21
Quantitative Comparison
22
Crowd Tracking
23
Spatial Proximity Constraint
24
Neighborhood Motion Effect
25
Grouping
26
Formulation
27
Appearance
28
Quadratic Constraints
29
Frank Wolfe Algorithm
30
Frank Wolfe with SWAP steps
31
Experiments . 9 high-density sequences
32
Quantitative Results
33
Contribution of each term
34
Summary
35
Future Work
Description:
Explore a doctoral dissertation defense on advanced multi-target tracking techniques for pedestrians. Delve into novel data association methods using Generalized Maximum Clique Problem (GMCP) and Generalized Maximum Multi Clique Problem (GMMCP) formulations. Learn about global optimization approaches incorporating motion and appearance, and discover solutions for tracking in extremely crowded scenes using Binary Quadratic Programming. Examine the limitations of current methods, proposed improvements, and experimental results across various datasets. Gain insights into cutting-edge algorithms for efficient tracking of hundreds of targets in complex scenarios like marathons and political rallies.

Global Data Association for Multiple Pedestrian Tracking

University of Central Florida
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