This course covers recent advances in online object tracking, focusing on tracking approaches, sparsity-based classifiers, discriminative models, occlusion handling, collaborative models, qualitative and quantitative evaluations, tracking by detection, compressive tracking, evaluation methodologies, and temporal robustness evaluation. The course aims to teach students the skills needed to understand and implement cutting-edge object tracking algorithms. The teaching method includes lectures, algorithm overviews, experimental results, and evaluation discussions. This course is intended for students and professionals in the field of computer vision and object tracking who want to stay updated on the latest advancements in the field.
Overview
Syllabus
Intro
Yosemite National Park
Lake Tahoe
Lake Mono and Parker Lake
Tracking Approaches
Related Work
Sparsity-based Classifier
Training Data
Discriminative Model: Summary
Occlusion Handling
New Histogram
Collaborative Model
Qualitative Evaluation
Quantitative Evaluation
Concluding Remarks
Outline
Tracking by Detection
Algorithm Overview
Two Components
Revisit MILTracker
Constructing Random Matrix R
Compressive Tracking/Sensing?
JL vs. RIP
Gaussian PDF Assumption
Experimental Results
Motivation
Evaluation Issues
Tracking Algorithms
Evaluated Algorithms
Evaluation Dataset
Evaluation Methodology
Temporal Robustness Evaluation
One Pass Evaluation
Low Resolution
Taught by
UCF CRCV
