# A Visual Object Tracking Algorithm Based on Improved TLD
## 1 Introduction  
Visual object tracking, as an important research field of computer vision, is a fundamental part of many computer vision systems, such as military navigation, human-computer interaction, unmanned aerial vehicle video analysis, dynamic behavioral recognition, intelligent medical treatment, and so on (see Figure 1) [1; 2; 3]. Given the location and extent of an arbitrary object, the task of visual object tracking is to determine the object's location with the best possible accuracy in the following frames [4]. Tracking results can be affected by several factors, such as illumination changes, occlusion, target nonrigid body deformation, and being out of view [5, 6]. Choosing suitable features to represent the target is of great significance for visual object tracking to face the abovementioned challenging factors. Several authors have reported that color can adapt to plastic deformation well [7]. However, it is not easy for color to discriminate an object from the background [8]. [PERSON] and [PERSON] [9] studied the histogram of oriented gradient (HOG) descriptor to represent an object by capturing the edge or gradient structure and demonstrated its robustness in illumination changes. More complex features were also suggested to represent the visual object, such as scale-invariant feature transform (SIFT) and speeded up robust features (SURF) [10]. Additionally, particular features are used in tracking specific objects. For example, Haar-like descriptors are widely used in face tracking systems [11]. Because the appearance of an object may change during a video, model adaption is employed in most advanced trackers to make use of the information present in later frames. Many researchers have shown great interest in visual object tracking and have proposed various trackers. For example, mean shift is a classical tracking algorithm. On the basis of the mean shift iterations, the most probable location of an object in a frame is found [7]. Particle-filtering-based trackers establish a certain model for objects and determine an object's location by calculating the similarity between the particles and the target [12]. Correlation-filtering-based trackers show great advantages in tracking speed; these include the circulant structure kernel (CSK) tracker, the kernelized correlation filter (KCF) tracker, and the discriminative scale space tracker (DSST) [12]. As the performance of computers improve, deep learning has been applied to the field of computer vision. For example, Siamese and end-to-end networks have been widely used for tracking [13, 14]. Among these trackers, the tracking-learning-detection (TLD) tracker proposed by [PERSON] et al. [15] introduced a novel tracking framework to solve the tracking problem. By combining a detector and a tracker with an online learning module, experiments showed that it performs well in long-term tracking. However, there are still some shortcomings that influence its actual application. The target is easily lost when the object is deformed or blurred. Further, the calculation is complicated, resulting in slow tracking speed. To improve the performance of the TLD tracker, [PERSON] et al. [16] studied the depth of images and applied it to enrich the object features. This proved to better represent an object in case of a complex background and is applicable to devices that can record depth information. A feature pool can be used for tracking a target to replace the object model in TLD and to select effective classifiers [17]. A feature pool helps to record the target characteristics more comprehensively. Nevertheless, collecting and maintaining the characteristics make the algorithm more complicated. [PERSON] [18] reduced the image resolution to cut down the number of image patches to detect, thereby enhancing the tracking speed. However, reducing the resolution results in a loss of object features, especially when the object is small in the field of view.