This paper reviews and evaluates several state-of-the-art online object tracking algorithms. Notwithstanding decades
of efforts, object tracking remains a challenging problem due to factors such as illumination, pose, scale, deformation,
motion blur, noise, and occlusion. To account for appearance change, most recent tracking algorithms focus on robust
object representations and effective state prediction. In this paper, we analyze the components of each tracking method and
identify their key roles in dealing with specific challenges, thereby shedding light on how to choose and design algorithms
for different situations. We compare state-of-the-art online tracking methods including the IVT,1 VRT,2 FragT,3 BoostT,4 SemiT,5 BeSemiT,6 L1T,7 MILT,8 VTD9 and TLD10 algorithms on numerous challenging sequences, and evaluate them
with different performance metrics. The qualitative and quantitative comparative results demonstrate the strength and
weakness of these algorithms.