1 research outputs found
Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking
We introduce a tracking-by-detection method that integrates a deep object
detector with a particle filter tracker under the regularization framework
where the tracked object is represented by a sparse dictionary. A novel
observation model which establishes consensus between the detector and tracker
is formulated that enables us to update the dictionary with the guidance of the
deep detector. This yields an efficient representation of the object appearance
through the video sequence hence improves robustness to occlusion and pose
changes. Moreover we propose a new state vector consisting of translation,
rotation, scaling and shearing parameters that allows tracking the deformed
object bounding boxes hence significantly increases robustness to scale
changes. Numerical results reported on challenging VOT2016 and VOT2018
benchmarking data sets demonstrate that the introduced tracker, L1DPF-M,
achieves comparable robustness on both data sets while it outperforms
state-of-the-art trackers on both data sets where the improvement achieved in
success rate at IoU-th=0.5 is 11% and 9%, respectively