# Fast and Robust Object Tracking Using Tracking Failure Detection in Kernelized Correlation Filter
## 1 Introduction  
Visual object tracking is one of the most active research topics in the computer vision field because of its wide application areas including: human-computer interaction, autonomous vehicle vision, visual surveillance, and robot vision, etc. In recent years, researchers have paid significant attention to correlation filter (CF)-based tracking algorithms [1; 2; 3]. The major advantage of the CF-based tracker is twofold: (i) computational efficiency by replacing the spatial correlation estimation process with element-by-element multiplication in the Fourier transform domain and (ii) being an online learning process that updates the position of the target object every frame.  
Despite a lot of studies in the visual tracking community, there are still several challenges such as appearance distortion, light change, rapid motion, motion blur, background clutters, deformation, occlusion, and rotation. Since the trackers trace the target based on the appearance and position of the target in the previous frames, tolerance against appearance and position of the target is a major factor which determines the accuracy of a tracker. Such challenging scenes result in significant mismatch between appearance of the real target and input object, which consequently yields the tracking failure. In this context, deep feature-based trackers have recently attracted increasing attention. Many convolutional neural network (CNN)-based trackers have been proposed to achieve higher accuracy than traditional CF-based trackers [4; 5]. The CNN-based trackers outperform CF-based trackers because deep features provide higher tolerance against challenging scenes.  
In spite of many advantages from the deep features, CNN-based trackers need to be trained using a very large dataset. Moreover, an excessive computational load is another disadvantage of the CNN-based trackers, which can process less than five frames per second using a personal computer with a single GPU. When real-time tracking is required, computational efficiency becomes the most important issue. Recently, a high performance camera with a frame rate up to 100 FPS or higher has been used for various applications, such as smart phones, action cams, and drones. In a video captured by such high-FPS cameras, appearance variation over frames are reduced whereas the high processing speed becomes more important. [PERSON] et al. presented that CF based trackers outperform deep feature based trackers not only in speed but also in accuracy under high FPS circumstances [6]. Furthermore, the use of one or more GPUs for a CNN-based tracker is another burden of a real-time, economic realization of practical video object tracking.  
In this paper, we propose a novel kernelized correlation filter (KCF)-based tracking method that improves tracking accuracy under challenging circumstances for high frame rates and real-time tracking. The proposed tracking algorithm processes each frame using three steps: (i) detection of tracking failure by analyzing the peak and average of neighboring correlation values, (ii) re-tracking the target using a tracking failure handing algorithm when the tracking failure is detected, and (iii) calculating a motion vector of the target for selecting preferred search window when tracking failure is detected.