A Comprehensive Review of Real-Time Vehicle Tracking for Smart Navigation Systems
Veena R S1, Seema Rani2.*, Sandeep Dalal2, Ch Madhava Rao3, Piyush Kumar Pareek4, Shweta Bansal5
1Department of Information Science and Engineering, Dayananda Sagar Academy of Technology and Management, Bengaluru -560082, Karnataka, Orchid - 0000-0003-1368-0634, India
2Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India
3Associate Professor, Dept. of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India
4Professor and Head Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru , Karnataka, India
5K R Managalam University, Gurugram, Haryana-122103, India
Emails: veena-ise@dsatm.edu.in; seema.rs.dcsa@mdurohtak.ac.in; sandeepdalal.80@gmail.com; cmadhavarao@kluniversity.in; piyush.kumar@nmit.ac.in; S.bansal6281@gmail.com
|
Abstract Vehicle tracking is one of computer vision's most important applications, with applications ranging from robotics and traffic monitoring to autonomous vehicle navigation and many more. Even with the significant advancements in recent research, issues like occlusion, fluctuating illumination, and fast motion still need to be addressed, calling for more investigation and creativity in this field. This study performs a thorough examination of various vehicle-tracking approaches and suggests a thorough classification scheme that divides them into four main categories: strategies that rely on features, segmentation, estimate, or learning. Two well-known methods are highlighted specifically in the estimation-based category: particle filters and Kalman filters. |
Keywords: Vehicle Tracking; Kalman Filter; Particle filter; Computer vision; smart navigation system