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We introduce a tracking-by-detection method that integrates a deep objectdetector with a particle filter tracker under the regularization frameworkwhere the tracked object is represented by a sparse dictionary. A novelobservation model which establishes consensus between the detector and trackeris formulated that enables us to update the dictionary with the guidance of thedeep detector. This yields an efficient representation of the object appearancethrough the video sequence hence improves robustness to occlusion and posechanges. Moreover we propose a new state vector consisting of translation,rotation, scaling and shearing parameters that allows tracking the deformedobject bounding boxes hence significantly increases robustness to scalechanges. Numerical results reported on challenging VOT2016 and VOT2018benchmarking data sets demonstrate that the introduced tracker, L1DPF-M,achieves comparable robustness on both data sets while it outperformsstate-of-the-art trackers on both data sets where the improvement achieved insuccess rate at IoU-th=0.5 is 11% and 9%, respectively.

Integration of Regularized l1 Tracking and Instance Segmentation for Video Object Tracking