Real-Time Thermal Infrared Tracking Based on Collaborative Online and Offline Method

红外图像实时在线离线跟踪算法研究

Received: 8 January 2018

Abstract

Most tracking-by-detection based trackers employ the online model update scheme based on the spatiotemporal consistency of visual cues. In presence of self-deformation, abrupt motion and heavy occlusion, these trackers suffer from different attributes and are prone to drifting. The model based on offline training, namely Siamese networks is invariant when suffering from the attributes. While the tracking speed of the offline method can be slow which is not enough for real-time tracking. In this paper, a novel collaborative tracker which decomposes the tracking task into online and offline modes is proposed. Our tracker switches between the online and offline modes automatically based on the tracker status inferred from the present failure tracking detection method which is based on the dispersal measure of the response map. The present Real-Time Thermal Infrared Collaborative Online and Offline Tracker (TCOOT) achieves state-of-the-art tracking performance while maintaining real-time speed at the same time. Experiments are carried out on the VOT-TIR-2015 benchmark dataset and our tracker achieves superior performance against Staple and Siam FC trackers by 3.3% and 3.6% on precision criterion and 3.8% and 5% on success criterion, respectively. The present method is real-time tracker as well.

摘要

大多数基于目标检测的红外图像目标跟踪算法采取基于时空一致性在线模型更新策略。然而，当所跟踪的目标发生形变、快速运动和受到遮挡时，在线模型更新过程会受到不同程度的干扰而导致目标跟踪失败。基于孪生网络的离线模型跟踪策略则能够在目标发生扰动的情况下保持其外观模型的不变性。然而，在跟踪速度上与在线模型更新策略差距较大。提出了目标跟踪过程中的跟踪错误检测方法将在线和离线目标模型更新方法相结合，该检测方法通过基于联合响应图的离散度测量来联合2类模型更新方法，并能根据当前目标跟踪状态自动在2种模型更新方法中切换，有效地解决了跟踪算法实时性与鲁棒性的平衡问题。所提出算法在VOT-TIR-2015数据库的实验结果显示相比原有算法Staple和SiamFC在跟踪成功率上分别提高3.3%和3.6%，在跟踪精度上分别提高3.8%和5%，同时保证跟踪的实时性。