# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 2 Related Work  
Various tracking approaches have been proposed to solve challenging problems, such as background clutters, partial occlusion, shape deformation, temporary disappearance, illumination changes, etc. [7; 8; 9]. Those trackers can be categorized based on several criteria such as on- or off-line training, handcrafted feature- or deep feature-based, and generative or discriminative model-based, etc. In online training, trackers are being trained with data acquired in the tracking process, while off-line trackers need a dataset for a priori training. To represent a target object, handcrafted feature-based trackers utilize features that are manually defined by researchers, whereas deep feature-based trackers utilize features extracted from a deep neural networks (DNNs). Discriminative model-based trackers distinguish target objects from background in an appropriate way, whereas generative model-based trackers search the best-matching window to trace the target. In this section, we summarize state-of-the-art tracking methods by classifying them into two categories: (i) correlation filter-based and (ii) deep learning-based methods. Correlation filter (CF) is one of the most popular approaches to visual tracking because of its robustness and computational efficiency. Basic mechanism of CF is to generate a response map which has large values in the region-of-interest (ROI), but small values in other regions. In this context, tracking is carried out by estimating position with the highest response value as the target location. To the best of the authors' knowledge, average synthetic exact filter (ASEF) proposed by [PERSON] et al. [10] and unconstrained minimum average correlation energy (UMACE) proposed by [PERSON] et al. [11] were the first correlation filter-based approaches to visual tracking. However, their training requirements are not suitable for online tracking. [PERSON] et al. [12] proposed an improved version of ASEF, called minimum output sum of square error (MOSSE)-based tracker. The major contribution of the MOSSE-based tracker is the on-line, adaptive training for appearance changes of the target object. After introduction of the MOSSE filter, CF-based tracking methods drew great attention in the visual tracking community and many modified versions of the CF-based tracker have been proposed in the literature. [PERSON] et al. proposed circulant structured with kernels (CSK) [1] method that exploits the correlation filter in kernel space, and kernelized correlation filter (KCF) [2] extends the kernel trick. In the KCF mechanism, the tracker generates a response map by multiplying the filter kernel and cropped search window at every frame in the Fourier domain, and then estimates the target position by searching the higher value in the inverse Fourier transformed response map. As the KCF achieved outstanding performance,researchers tried to improve KCF based tracking method, resulting in many derivational methods. [PERSON] et al. [13] tried to handle scale variation by training an adaptive multi-scale correlation filter using HOG feature, and [PERSON] et al. [14] tried to solve the partial occlusion problem using multiple correlation filters. [PERSON] et al. [5] proposed hedged deep tracker (HDT), in which several weak trackers are combined to realize a strong tracker. In other words, target positions are estimated from weak trackers, and a final decision is made by the hedge algorithm that combines weak trackers. [PERSON] et al. [15] proposed long-term correlation tracker (LCT), which is robust against target translation and can perform re-detection if tracking failure occurs. [PERSON] et al. [16] presented sum of template and pixel-wise learners (STAPLE) method using HOG and color histograms for target representation. More recently, [PERSON] et al. [17] proposed Parallel Correlation Filters, [PERSON] et al. [18] presented a tracker using correlation filter fused with color histogram and [PERSON] et al. [19] proposed a correlation filter-based tracker using enhanced features and adaptive Kalman filter.