基于异步相关判别性学习的孪生网络目标跟踪算法

现有基于孪生网络的单目标跟踪算法能够实现很高的跟踪精度,但是这些跟踪器不具备在线更新的能力,而且其在跟踪时很依赖目标的语义信息,这导致基于孪生网络的单目标跟踪算法在面对具有相似语义信息的干扰物时会跟踪失败.为了解决这个问题,提出了一种异步相关响应的计算模型,并提出一种高效利用不同帧间目标语义信息的方法.在此基础上,提出了一种新的具有判别性的跟踪算法.同时为了解决判别模型使用一阶优化算法收敛慢的问题,使用近似二阶优化的方法更新判别模型.为验证所提算法的有效性,分别在Got-10k、TC128、OTB和VOT2018数据集上做了对比实验,实验结果表明,该方法可以明显地改进基准算法的性能.

Design of Asynchronous Correlation Discriminant Single Object Tracker Based on Siamese Network

The existing single target object tracking algorithms based on the siamese network can achieve very high tracking performance, but these trackers can not update online, and they heavily rely on the semantic information of the target in tracking. It caused the trackers, which based on the siamese network, fail when facing the disruptor who has similar semantic information. To address this issue, this paper proposes an asynchronous correlation response calculation model and an efficient method of using the target＇s semantic information in different frames. Based on this, a new discriminative siamese network-based tracker is proposed. To address the convergence speed issue in the traditional first-order optimization algorithm, an approximate second-order optimization method is introduced to update the discriminant model online. To evaluate the effectiveness of the proposed method, comparison experiments on Got-10k, TC128, OTB, and VOT2018 between the proposed tracker and other lastest state-of-the-art trackers are adopted. The experimental results demonstrate that the proposed method can significantly improve the performance of the baseline.