## 1 Introduction  
Tracking the position of objects of interest from a sequence of video frames is a fundamental problem in computer vision research. Object tracking is an integral part of computer vision and is applied in various fields including robotics, surveillance system, motion analysis, autonomous cars, unmanned aerial vehicles (UAVs) and human computer interaction (HCI). However, the research on object tracking is still recognized as a difficult problem since the object tracking environment contains various challenging factors, such as illumination variation, scale variation, occlusion, deformation, motion blur, fast motion and rotation. These factors significantly degrade the performance of object tracking. For that reason, minimizing the influence of environmental changes in the development of robust trackers is an important issue. There are many tracking algorithms [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37] to deal with the variety of environmental changes. The state-of-the-art tracking algorithms have tried to solve the problem by analyzing the cause of environmental changes using various classification approaches. In this paper, we present a novel correlation filter-based object tracking algorithm that focuses on solving scale variation and partial occlusion problems. The proposed algorithm is based on a discriminative model tracker with a correlation filter. The kernelized correlation filter (KCF) tracker [21] has demonstrated outstanding performance for object tracking by drastically reducing computational cost using an efficient search based on the diagonalization property of a circular matrix and a dual correlation filter (DCF). However, the KCF tracker is sensitive to environment changes because it still does not consider partial occlusion and scale variation, which make the performance of the tracker poor. Most tracking-by-detection algorithms consider only object translation [22], but the proposed algorithm deals with scaling and partial occlusion, as well as object translation. Partial occlusion is a significant problem that degrades the performance of object detection. We propose a robust KCF-based tracker to overcome the partial occlusion problem using a partial block scheme. The partial block scheme facilitates stable object tracking, even if the object is partially occluded. A robust tracker also needs a strategy for scale estimation to deal with changes of the object size. The scale space [22] creates an image pyramid and determines the most appropriate size of an object block. We also propose an adaptive update model using a weighting function by improving the general update model used in the original version of the kernelized correlation filter [21]. The proposed adaptive update model calculates the learning rate with a modified sigmoid function as the weighting function, and then, the optimal learning rate is calculated for each frame. In summary, the proposed method is developed to deal with partial occlusion, scaling, illumination variation and deformation. Experimental results demonstrated that the proposed method exhibits a better performance than existing state-of-the-art algorithms for various test videos including illumination variation, scale variation, occlusion, deformation, motion blur, fast motion and rotation. Figure 1 compares the performances of the proposed method and state-of-the-art trackers. More specifically, Tiger2 including partial occlusion substantiates that the proposed partial block scheme successfully solves the partial occlusion problem. Freeman3 and Shaking substantiate that the performance of the proposed method is good enough for the scale variation problem. On the other hand, existing trackers do not properly respond to occlusion and scale variation problems.