This document discusses object tracking and summarizes some key approaches: 1. Object tracking methods can be categorized as either online and parts-based or model-based and holistic. Popular online parts-based trackers include WSL, Online Boosting, and MILTrack. Well-known model-based holistic trackers are IVT. 2. Transferring visual priors learned offline can be used to represent objects online for tracking with updates. 3. The document outlines preliminary results from a library called TrackLab that contains 5 tracking algorithms and discusses performance evaluation benchmarks, criteria, and attributes.

1. Learning to Track Objects
Qing Wang Ming-Hsuan Yang
University of California at Merced

2. Online and Model-based
• Spectrum Parts(features)-based
WSL [Jepson CVPR 01]
… Online Boosting [Grabner CVPR06]
MILTrack [Babenko PAMI 11]
Model-based Online
… IVT [Ross IJCV08], …
Holistic
• Object detection, recognition and tracking
• Bridge the gaps

3. Transferring Visual Prior
• Image patches from real-world image data often share great similarity
• Visual prior
• Learned offline to exploit structural similarity
• Used for object representation in online object tracking with update

4. Preliminary Results
David_indoor
2004

5. Preliminary Results

6. TrackLab
• A library of tracking code
– Ongoing work, now with 5 algorithms
• Performance evaluation
– Benchmark data sets
– Empirical
– Theatrical(w/assumption?):performance guarantee
• Ensemble of heterogeneous trackers

7. Performance Evaluation
• Taxonomy

8. Performance Evaluation
• Benchmark data sets
– Build on top of existing data sets (PETS, AVSS, TRECVID, LabelMe Video, etc.)
• Characteristics, attributes:

9. Performance Evaluation
• Criteria
• Much more work to be done