  Unmanned aerial vehicle (UAV) based visual tracking has been confronted with
numerous challenges, e.g., object motion and occlusion. These challenges
generally introduce unexpected mutations of target appearance and result in
tracking failure. However, prevalent discriminative correlation filter (DCF)
based trackers are insensitive to target mutations due to a predefined label,
which concentrates on merely the centre of the training region. Meanwhile,
appearance mutations caused by occlusion or similar objects usually lead to the
inevitable learning of wrong information. To cope with appearance mutations,
this paper proposes a novel DCF-based method to enhance the sensitivity and
resistance to mutations with an adaptive hybrid label, i.e., MSCF. The ideal
label is optimized jointly with the correlation filter and remains temporal
consistency. Besides, a novel measurement of mutations called mutation threat
factor (MTF) is applied to correct the label dynamically. Considerable
experiments are conducted on widely used UAV benchmarks. The results indicate
that the performance of MSCF tracker surpasses other 26 state-of-the-art
DCF-based and deep-based trackers. With a real-time speed of _38 frames/s, the
proposed approach is sufficient for UAV tracking commissions.
