Smart Traffic Management of Vehicles using Faster R-CNN based Deep Learning Method

TL;DR

This paper presents a novel deep learning framework using Faster R-CNN and active nets for vehicle segmentation in traffic videos, addressing challenges like occlusion and background clutter to improve accuracy.

Contribution

It introduces a four-step vehicle segmentation method combining adaptive background modeling, Faster R-CNN, and active nets, enhancing segmentation accuracy in complex traffic scenarios.

Findings

Higher segmentation accuracy achieved

Effective handling of occlusions and background clutter

Superiority demonstrated over existing methods

Abstract

With constant growth of civilization and modernization of cities all across the world since past few centuries smart traffic management of vehicles is one of the most sorted after problem by research community. It is a challenging problem in computer vision and artificial intelligence domain. Smart traffic management basically involves segmentation of vehicles, estimation of traffic density and tracking of vehicles. The vehicle segmentation from traffic videos helps realization of niche applications such as monitoring of speed and estimation of traffic. When occlusions, background with clutters and traffic with density variations are present, this problem becomes more intractable in nature. Keeping this motivation in this research work, we investigate Faster R-CNN based deep learning method towards segmentation of vehicles. This problem is addressed in four steps viz minimization with adaptive background model, Faster R-CNN based subnet operation, Faster R-CNN initial refinement and result optimization with extended topological active nets. The computational framework uses ideas of adaptive background modeling. It also addresses shadow and illumination related issues. Higher segmentation accuracy is achieved through topological active net deformable models. The topological and extended topological active nets help to achieve stated deformations. Mesh deformation is achieved with minimization of energy. The segmentation accuracy is improved with modified version of extended topological active net. The experimental results demonstrate superiority of this computational framework