@article{MAKHILLRJAS201914110154,
    title = {An Efficient Object Detection and Tracking System Based on
Immune Genetic Algorithm},
    journal = {Research Journal of Applied Sciences},
    volume = {14},
    number = {1},
    pages = {7-15},
    year = {2019},
    issn = {1815-932x},
    doi = {rjasci.2019.7.15},
    url = {https://makhillpublications.co/view-article.php?issn=1815-932x&doi=rjasci.2019.7.15},
    author = {Madhavi and},
    keywords = {Particle filter algorithm,immune Genetic algorithm,motion segmentation,noise removal,re-sampling,efficiently},
    abstract = {Particle filter algorithm is widely used for target tracking using video sequences which is of great
importance for intelligent surveillance applications. However, there is still much room for improvement, e.g., the
so-called "sample impoverishment". A novel algorithm, the Immune Genetic Algorithm (IGA) is proposed based
on the theory of immunity in biology which mainly constructs an immune operator accomplished by two steps:
a vaccination and an immune selection. In our proposed methodology a sample video is given as input for the
purpose of object tracking. The steps to be carried out in our proposed method are given: motion segmentation,
noise removal and object tracking. Motion segmentation involves the generation of background model for the
purpose of segmentation. Noise removal involves the particle filters. The main process that takes place in the
particle filter is re-sampling. Then the object was tracked by using immune Genetic algorithm. Immune Genetic
algorithm involves vaccination and the immune selection process including the process of ordinary Genetic
algorithm. This efficiently involves the multiple object detection.}
    }