Blog
About

5
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Learning regression and verification networks for long-term visual tracking

      Preprint

      , , , ,

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          In the long-term single object tracking task, the target moves out of view frequently. It is difficult to determine the presence of the target and re-search the target in the entire image. In this paper, we circumvent this issue by introducing a collaborative framework that exploits both matching mechanism and discriminative features to account for target identification and image-wide re-detection. Within the proposed collaborative framework, we develop a matching based regression module and a classification based verification module for long-term visual tracking. In the regression module, we present a regressor that conducts matching learning and copes with drastic appearance changes. In the verification module, we propose a classifier that filters out distractions efficiently. Compared to previous long-term trackers, the proposed tracker is able to track the target object more robustly in long-term sequences. Extensive experiments show that our algorithm achieves state-of-the-art results on several datasets.

          Related collections

          Author and article information

          Journal
          12 September 2018
          Article
          1809.04320

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          Custom metadata
          6 pages
          cs.CV

          Computer vision & Pattern recognition

          Comments

          Comment on this article