A deformable 15D approach for localization and recognition of road traffic monocular images

This paper presents a strategic approach for localizing and recognizing the vehicles amidst the traffic scenes generated by monocular camera or video. Previous studies on localization and recognition of vehicles are Model based recognition, 3D triangle based modeling, Model based on Wheel alignment, Ferryman 29D PCA coefficient model and etc. The disadvantages of above listed proposals are Affine transformation issues, redundant Data's, Noise in computation, inability to arrive at accurate shape parameters, poor occlusion detection and too much of modeling's. This paper addresses the above issues and proposes a Deformable Efficient local Gradient based method for localizing the vehicle and Evolutionary Fitness evaluation method with EDA for recognizing exact vehicle model from the traffic scenes. Each images are projected (12D + 3D = 15D) in the image plane. Since the vehicle moves over the ground plane, the pose of the vehicle is determined by position co-efficient X, Y and orientation Θ (3D), the 12 parameters are the parameters of Shape, and it is set up as the prior information based on the mined rules for vehicle localization and continuous EDA approach for vehicle recovery. The system also deals with occlusion of related structures based on stochastic analysis.