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.

Recently, the performance advancement of discriminative correlation filter (DCF) based trackers is predominantly driven by the use of deep convolutional features. As convolutional features from multiple layers capture different target information, existing works integrate hierarchical convolutional features to enhance target representation. However, these works separate feature integration from DCF learning and hardly benefit from end-to-end training. In this letter, we incorporates feature integration and DCF learning in a unified convolutional neural network. This network reformulates feature integration as a differential module that concatenates features from the shallow and deep layers. A channel attention mechanism is introduced to adaptively impose channel-wise weight on the integrated features. Experimental results on OTB100 and UAV123 demonstrate that our method achieves significant performance improvement while running in real-time.