@article{MAKHILLJEAS201914117325,
    title = {Kernelized Correlation Filters Parameters Optimization for
Enhanced Visual Tracking},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {1},
    pages = {293-307},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.293.307},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.293.307},
    author = {Parvathy and},
    keywords = {CLE,parameter optimization,kernelized correlation filters,computer vision,Visual tracking,OR},
    abstract = {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 OR and lower CLE 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 research. Through the optimization, the overall OR is increased to 0.554
and overall CLE 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.}
    }