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

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.

Keywords

• thermal infrared tracking
• staple
• siamese network
• failure tracking detection