Mutation Sensitive Correlation Filter for Real-Time UAV Tracking with Adaptive Hybrid Label
This work addresses tracking challenges for UAV applications, offering an incremental improvement over existing DCF-based methods by enhancing sensitivity to appearance changes.
The paper tackled the problem of tracking objects from UAV videos, where appearance mutations cause tracking failure, by proposing a mutation-sensitive correlation filter with an adaptive hybrid label (MSCF) that achieved state-of-the-art performance, surpassing 26 other trackers with a real-time speed of 38 frames/s.
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.