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on
1
Intro
2
Yosemite National Park
3
Lake Tahoe
4
Lake Mono and Parker Lake
5
Tracking Approaches
6
Related Work
7
Sparsity-based Classifier
8
Training Data
9
Discriminative Model: Summary
10
Occlusion Handling
11
New Histogram
12
Collaborative Model
13
Qualitative Evaluation
14
Quantitative Evaluation
15
Concluding Remarks
16
Outline
17
Tracking by Detection
18
Algorithm Overview
19
Two Components
20
Revisit MILTracker
21
Constructing Random Matrix R
22
Compressive Tracking/Sensing?
23
JL vs. RIP
24
Gaussian PDF Assumption
25
Experimental Results
26
Motivation
27
Evaluation Issues
28
Tracking Algorithms
29
Evaluated Algorithms
30
Evaluation Dataset
31
Evaluation Methodology
32
Temporal Robustness Evaluation
33
One Pass Evaluation
34
Low Resolution
Description:
Explore recent advancements in online object tracking through this comprehensive guest lecture by Dr. Ming-Hsuan Yang at the University of Central Florida. Delve into various tracking approaches, including sparsity-based classifiers, discriminative models, and occlusion handling techniques. Examine the collaborative model, qualitative and quantitative evaluations, and tracking by detection methods. Learn about compressive tracking, Gaussian PDF assumptions, and experimental results. Gain insights into evaluation issues, methodologies, and datasets used in tracking algorithms. Discover temporal robustness evaluation techniques and one-pass evaluation methods for low-resolution scenarios.

Recent Advances in Online Object Tracking

University of Central Florida
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