OPTIMIZING VEHICLE CLASSIFICATION USING BIO STATISTICAL ALGORITHM

Abstract

The intelligent transportation management is expected to address issues like road planning, on-thefly vehicle identification, road maintenance, smart traffic diversions, etc. Detection and classification
of vehicles are important in planning traffic control and in gathering traffic statistics information that
can be used in intelligent transport systems. Hence town planning requires vehicle detection. The
most superficial model features offline learning-based vehicle detection techniques which cannot meet
the real-world challenges of environmental complexity and dynamics of the scene. Bio-inspired
algorithms are highly efficient that acts as a platform based on the various biological evolution of
nature to develop new and robust computing techniques. Focusing on these problems, the vehicle
detector finds out each vehicle's traffic status which includes both wanted and unwanted classification
details and hence an efficient algorithm has to be developed to ensure the accurate vehicle detection
strategy. Many preexisting algorithms reveal that the classification outcomes are non-uniform for the
4 classes. Hence a BIO STATISTICAL algorithm based on Linear Discriminant Algorithm (LDA) and
Particle Swarm Optimization (PSO) is structured to systematically increase performance of all 4
classes in the classification.