# Siamese Tracking with Adaptive Template-Updating Strategy
## 1 Introduction  
Visual-object tracking is one of the most fundamental problems in computer vision and has attracted much attention in recent years. A wide range of applications relies on tracking methods such as human-motion analyses [1], robotic services [2], human-computer interactions [3], and surveillance [4]. In this paper, we focus on single-object tracking with the target given in the first frame, which is the most common scenario of tracking.  
There are two aspects to improve target-locating precision and tracking robustness in existing tracking methods. One is to design effective algorithms of either generative [5; 6; 7; 8] or discriminative [9; 10; 11; 12; 13; 14; 15] models; the other is to optimize target representation by extracting both conventional handcrafted features and deep convolutional features. As a result, complicated tracking algorithms or features improve performance but simultaneously increase computational complexity. However, the most difficult and significant problem in tracking application is to balance performance and speed. Unlike state-of-the-art DCF methods such as LADCF [16] and MFT, which fuse multiresolution and multifeature to improve performance on rank and sacrifice algorithm speed, Siamese tracking-network [17; 18; 19; 20; 21; 22; 23] approaches obtain balanced accuracy and speed. To be specific, GOTURN [18], SiamFC [19], and RASNet [20] transfer traditional tracking tasks from a target-searching and regressing process into a similarity-comparing function and complete tracking in each frame within a fixed amount of time. DSiam [7] adopts online target-variation transformation andbackground-suppression transformation, which make DSiam adapt target changes while excluding background interference.  
There are three problems that should be addressed, even though these Siamese tracking approaches have balanced speed and accuracy: first of all, most of these Siamese network trackers are not able to update the templates. On the one hand, their simple fixed models [17; 18; 19; 20; 23] guarantee high tracking speed; on the other hand, they sacrifice better adapting to target-appearance deformation. Second, the performance of most Siamese tracking networks cannot be guaranteed, especially with cluttered backgrounds, because of the interference of semantic backgrounds. In other words, conventional bounding-box inputs are not able to separate background distractions. Last, out-of-view and full-occlusion conditions are still great challenges due to the local-search strategy that is employed by most Siamese trackers.  
Our contributions are threefold: (1) We designed and implemented the whole Siamese tracking system with an adaptive template-updating strategy. Single-point initialization, on the one hand, took advantage of our contour templates over conventional bounding-box templates; on the other hand, it achieved the function of our start-up in the system. (2) We designed and combined a target-and a contour-detection network to compose our template-update network that was able to adaptively update templates according to different conditions. (3) Our method achieved leading performance regarding vehicle video images in both the OTB100 and the Defense Advanced Research Projects Agency (DARPA) dataset.