<b>A Novel GA SVM Model For Vehicles And Pedestrial Classification In Videos</b> The paper presents a novel algorithm for object classification in videos based on improved support vector machine SVM and genetic algorithm. One of the problems of support vector machine is selection of the appropriate parameters for the kernel. This has affected the accuracy of the SVM over the years. This research aims at optimizing the SVM Radial Basis kernel parameters using the genetic algorithm. Moving object classification is a requirement in smart visual surveillance systems as it allows the system to know the kind of object in the scene and be able to recognize the actions the object can perform. This paper presents an GA SVM machine learning approach for real time object classification in videos. Radial distance signal features are extracted from the silhouettes of object detected in videos. The radial distance signals features are then normalized and fed into the GA SVM model. The classification rate of 99.39 is achieved with the genetically trained SVM algorithm while 99.1 classification accuracy is achieved with the normal SVM. A comparison of this classifier with some other classifiers in terms of classification accuracy shows a better performance than other classifiers such as the normal SVM, Artificial neural network ANN , Genetic Artificial neural network GANN , K nearest neighbor K NN and K Means classifiers. Support Vector Machines, Genetic Algorithms, Neural networks, Classification, Kernel Function, Fitness, Cross Validation Issue-4 Volume-1 Akintola Kolawole G.