Kernerlized Correlation Filters Parameters Optimization For Enhanced Visual Tracking - Ong, Chor Keat (Universiti Sains Malaysia, 2017)
Visual tracking has become one of the most important components in computer vision as the knowledge in this field can be applied into a wide range of applications in computer vision such as medical imaging, pattern recognition, video surveillance, industrial robot, computer-human interaction, etc. A lot of researches have been conducted and many types of state-of-the-art methods and modifications such as sparse representation, online similarity learning, self-expressive, spatial kernel phase correlation filter and others are proposed in order to increase the robustness of the tracking. Despite of many methods has been demonstrated successfully, but there are several issues that still need to be addressed. There still have some unsolvable difficulties in which they become a challenging task to track an object effectively and robustly and it will tend to decrease the accuracy of the results and hence. Until now, there are still no perfect algorithm to track the target flawlessly. In order to improve the performance, the main idea proposed is implementing optimization technique on the selected parameters and obtain a better performance. In this research, the tracking is proposed by using the overlap ratio (OR) and centre location error (CLE). In our case, our target is to obtain a better accuracy, which is higher overlap ratio and lower centre location error than the result from the algorithms available in public. A simple optimization is used in here, where the global best results with respect to the value of the parameters are selected through a range of values defined in our work. Through the optimization, the overall overlap ratio is increased to 0.554 and overall centre location error is decreased to 19.803 pixels. Thus, the proposed method had increased the accuracy and robustness of the visual tracking on many of the video sequences.