Mengdan Zhang

TL;DR: The Visual Object Tracking challenge VOT2016 goes beyond its predecessors by introducing a new semi-automatic ground truth bounding box annotation methodology and extending the evaluation system with the no-reset experiment.

TL;DR: The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative; results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years.

TL;DR: This work presents an end-to-end lightweight network architecture, namely DCFNet, to learn the convolutional features and perform the correlation tracking process simultaneously, and treats DCF as a special correlation filter layer added in a Siamese network.

TL;DR: A dual-direction unit $\ell_{1}$-norm constrained tensor power iteration algorithm is proposed and a deep pair-wise appearance similarity metric based on object mask is presented in this paper where just the features from true target region are utilized.

TL;DR: This work presents a fully functional correlation filter based tracking algorithm which is able to simultaneously model target appearance changes from spatial displacements, scale variations, and rotation transformations, and is efficiently and effectively performed in the joint space with fast Fourier Transform.