Fragments based tracking with adaptive cue integration

In this paper, we address the issue of part-based tracking by proposing a new fragments-based tracker. The proposed tracker enhances the recently suggested FragTrack algorithm to employ an adaptive cue integration scheme. This is done by embedding the original tracker into a particle filter framework, associating a reliability value to each fragment that describes a different part of the target object and dynamically adjusting these reliabilities at each frame with respect to the current context. Particularly, the vote of each fragment contributes to the joint tracking result according to its reliability, and this allows us to achieve a better accuracy in handling partial occlusions and pose changes while preserving and even improving the efficiency of the original tracker. In order to demonstrate the performance and the effectiveness of the proposed algorithm we present qualitative and quantitative results on a number of challenging video sequences.

► Each fragment describing a different object part has a reliability value.
► Current visual context dynamically adjusts fragment reliabilities for adaptive cue integration.
► Each fragment contributes to the joint tracking result relative to its reliability.
► Fragments-based tracker attains a good tracking accuracy under difficult scenarios.
► Dynamic reliability maps provide additional information about occluded object parts.