Siamese deep-network trackers have received significant attention in recent years due to their real-time speed and state-of-the-art performance. However, Siamese trackers suffer from similar looking confusers, that are prevalent in aerial imagery and create challenging conditions due to prolonged occlusions where the tracker object re-appears under different pose and illumination. Our work proposes SiamReID, a novel re-identification framework for Siamese trackers, that incorporates confuser rejection during prolonged occlusions and is well-suited for aerial tracking. The re-identification feature is trained using both triplet loss and a class balanced loss. Our approach achieves state-of-the-art performance in the UAVDT single object tracking benchmark.
翻译:近年来,暹罗人深网络跟踪器因其实时速度和最新性能而备受关注,然而,暹罗人跟踪器也存在相似的图象混淆,在空中图像中普遍存在,由于长期隔离,跟踪器物体重新出现在不同面貌和照明下,从而创造了具有挑战性的条件。我们的工作提议,SiamReID是暹罗人跟踪器的新型再识别框架,在长期隔离期间包含迷惑式排斥,适合空中跟踪。再识别功能是使用三重损失和阶级平衡损失来培训的。我们的方法在UAVDT单一目标跟踪基准中实现了最先进的性能。