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.
翻译:无人驾驶航空飞行器(UAV)基于视觉跟踪(UAV)的无人驾驶航空飞行器(UAV)已经面临许多挑战,例如物体运动和隔离等,这些挑战通常会带来目标外观出意想不到的突变,并导致跟踪失败;然而,由于预先定义的标签,通常只集中在培训地区的中心,以歧视性相关过滤器(DCF)为基础的跟踪器对目标突变不敏感;同时,由于隔离或类似物体造成的外观突变通常导致不可避免地了解错误信息;为了应对外观变化,本文件建议采用基于DCF的新方法,即MSCF,以提高对适应性混合标签(即MSCF)的突变的敏感度和抵抗力。理想标签与相关过滤器共同优化,并保持时间一致性。此外,对称为突变威胁因的突变因素(MTF)进行创新的测量,以动态方式纠正标签。对广泛使用的UAVV基准进行了大量试验。结果显示,MSCF追踪器的性能超过其他26个州级、基于DF的深基跟踪器,对拟议的UA的实时跟踪器采用足够速度。