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Unmanned aerial vehicle (UAV) based visual tracking has been confronted withnumerous challenges, e.g., object motion and occlusion. These challengesgenerally introduce unexpected mutations of target appearance and result intracking 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 theinevitable learning of wrong information. To cope with appearance mutations,this paper proposes a novel DCF-based method to enhance the sensitivity andresistance to mutations with an adaptive hybrid label, i.e., MSCF. The ideallabel is optimized jointly with the correlation filter and remains temporalconsistency. Besides, a novel measurement of mutations called mutation threatfactor (MTF) is applied to correct the label dynamically. Considerableexperiments are conducted on widely used UAV benchmarks. The results indicatethat the performance of MSCF tracker surpasses other 26 state-of-the-artDCF-based and deep-based trackers. With a real-time speed of _38 frames/s, theproposed approach is sufficient for UAV tracking commissions.

Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label