In this paper, a new method known as Grid Base Classifier was proposed. This method carries the advantages of the two previous methods in order to improve the classification tasks. The problem with the current lazy algorithms is that they learn quickly, but classify very slowly. On the other hand, the eager algorithms classify quickly, but they learn very slowly. The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. The method was developed based on the grid structure which was done to create a powerful method for classification. In the current research, the new algorithm was tested and applied to the multiclass classification of two or more categories, which are important for handling problems related to practical classification. The new method was also compared with the Levenberg-Marquardt back-propagation neural network in the learning stage and the Condensed nearest neighbour in the generalization stage to examine the performance of the model. The results from the artificial and real-world data sets (from UCI Repository) showed that the new method could improve both the efficiency and accuracy of pattern classification.
CCTV surveillance systems are widely used as a street monitoring tool in public and private areas. This
paper presents a novel approach of an intelligent surveillance system that consists of adaptive background
modelling, optimal trade-off features tracking and detected moving objects classification. The proposed
system is designed to work in real-time. Experimental results show that the proposed background
modelling algorithms are able to reconstruct the background correctly and handle illumination and adverse
weather that modifies the background. For the tracking algorithm, the effectiveness between colour,
edge and texture features for target and candidate blobs were analysed. Finally, it is also demonstrated
that the proposed object classification algorithm performs well with different classes of moving objects
such as, cars, motorcycles and pedestrians.