Name
Abstract | ||
|---|---|---|
This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework. The success of MHT largely depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors that are currently available. We demonstrate that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets. In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis. We show that appearance models can be learned efficiently via a regularized least squares framework, requiring only a few extra operations for each hypothesis branch. We obtain state-of-the-art results on popular tracking-by-detection datasets such as PETS and the recent MOT challenge. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICCV.2015.533 | ICCV |
DocType | Volume | Issue |
Conference | 2015 | 1 |
ISSN | Citations | PageRank |
1550-5499 | 62 | 1.76 |
References | Authors | |
34 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chanho Kim | 1 | 83 | 5.57 |
| Fuxin Li | 2 | 772 | 52.53 |
| Arridhana Ciptadi | 3 | 152 | 5.39 |
| James M. Rehg | 4 | 5259 | 474.66 |