When Correlation Filters Meet Siamese Networks for Real-Time Complementary Tracking

Abstract

Discriminative correlation filter (DCF)-based trackers have recently exhibited high efficiency and impressive robustness to challenging factors, such as illumination change and partial occlusion. However, in cases with fast motion and full occlusion, these trackers drift off soon and can hardly re-detect the target from the restricted search region due to the boundary effect. On the contrary, recent work using a fully convolutional Siamese network (Siamfc) locates the exemplar image within a large search image but suffers from coarse location and distractors. In this paper, we propose a real-time complementary tracker (RCT) by integrating DCF and Siamfc into a two-stage tracking framework where DCF and Siamfc share mutual advantages and complement each other. In the first stage of this framework, RCT locates the target coarsely but robustly with Siamfc. In the second stage, the derived coarse location is refined by DCF for higher accuracy. For efficiency reasons, Siamfc in the first stage is activated occasionally based on the tracking status inferred from the correlation response map of DCF in the second stage. Comprehensive experiments are performed on three popular benchmark datasets: OTB2013, OTB2015, and VOT2016. On OTB2013, RCT runs with over 40 f/s and achieves an absolute gain of 4.8% and 5.2% in mean overlap precision compared with two base trackers (Staple and Siamfc). On VOT2016, RCT makes a good balance between performance and efficiency, ranking fifth in EAO and first in EFO compared with the top five trackers.