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.
Madhavi Gajula and A. Jhansi Rani, 2019. An Efficient Object Detection and Tracking System Based on Immune Genetic Algorithm. Research Journal of Applied Sciences, 14: 7-15.