Dynamic Siamese Network With Adaptive Kalman Filter for Object Tracking in Complex Scenes

Due to the deficiency of prior information for online updating process, the tracking accuracy of fully-convolutional Siamese network (SiamFC) in complex scenes such as similar object interference, fast moving, and appearance change is not good. To solve the problem, a new object tracker based on a dynamic template updating strategy and the re-location mechanism based on the adaptive Kalman is proposed. To suppress the object interference and overcome the instability of fast-moving object tracking, an adaptive Kalman filter method is designed to change the selection method of search region and select the bounding box of the object closest to the predicted position. For the adaptation of appearance change, the high-confidence tracking results are fused with the initial template to dynamic update the template. Compared with traditional Kalman filter, the expectation of residual error for the adaptive Kalman filter method can be controlled in a low range by the adjustment of the gain online. The introduction of the adaptive Kalman based re-location mechanism improves the discriminative ability of SiamFC in interference scene. With the dynamic template updating strategy, the tracker obtains strong generalization capability to adapt to the appearance change of the tracking target. It is demonstrated that the proposed method performs real-time object tracking at the speed of 43fps and achieves competitive performance on OTB, VOT and TC128 datasets compared with other state-of-the-art trackers.