English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents two visual trackers from the different paradigms oflearning and registration based tracking and evaluates their application inimage based visual servoing. They can track object motion with four degrees offreedom (DoF) which, as we will show here, is sufficient for many finemanipulation tasks. One of these trackers is a newly developed learning basedtracker that relies on learning discriminative correlation filters while theother is a refinement of a recent 8 DoF RANSAC based tracker adapted with a newappearance model for tracking 4 DoF motion.  Both trackers are shown to provide superior performance to several state ofthe art trackers on an existing dataset for manipulation tasks. Further, a newdataset with challenging sequences for fine manipulation tasks captured fromrobot mounted eye-in-hand (EIH) cameras is also presented. These sequences havea variety of challenges encountered during real tasks including jittery cameramovement, motion blur, drastic scale changes and partial occlusions.Quantitative and qualitative results on these sequences are used to show thatthese two trackers are robust to failures while providing high precision thatmakes them suitable for such fine manipulation tasks.

4-DoF Tracking for Robot Fine Manipulation Tasks