Name
Abstract | ||
|---|---|---|
We introduce a novel probabilistic tracking algorithm that incorporates combinatorial data association constraints and model-based track management using variational Bayes. We use a Bethe entropy approximation to incorporate data association constraints that are often ignored in previous probabilistic tracking algorithms. Noteworthy aspects of our method include a model-based mechanism to replace heuristic logic typically used to initiate and destroy tracks, and an assignment posterior with linear computation cost in window length as opposed to the exponential scaling of previous MAP-based approaches. We demonstrate the applicability of our method on radar tracking and computer vision problems. |
Year | Venue | Field |
|---|---|---|
2014 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 27 (NIPS 2014) | Mathematical optimization,Heuristic,Exponential function,Radar tracker,Computer science,Data association,Artificial intelligence,Probabilistic logic,Scaling,Machine learning,Computation,Bayes' theorem |
DocType | Volume | ISSN |
Conference | 27 | 1049-5258 |
Citations | PageRank | References |
2 | 0.35 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Turner, Ryan D. | 1 | 34 | 4.33 |
| Bottone, Steven | 2 | 2 | 0.69 |
| Avasarala, Bhargav | 3 | 2 | 0.35 |