A Study and Analysis on Pedestrian Detection and Tracking Through Rear-View Images
Damineni Sree Lakshmi, Adusumilli Divya, Emandi Sreedevi, K. Ravikiran, Prasanthi Gottumukkala, Akhilnath Muddana · Ingénierie des systèmes d information · 2023
As indicated by the Transportation Research and Industry Prevention Programme (TRIPP)'s Road Safety in India Report-2020, 33% of the accidents victims (deaths) are pedestrians.Heavy vehicles as well as cars are not able track pedestrian's movements on time.Most of the Children met with the accidents due to vehicle reversing.This problem motivates to track pedestrian through rear-view in heavy vehicles as well as for cars.Certain machine learning and deep learning approaches will best adapt to coping with the particular problems of rear-view pedestrian detection.In this work a literature survey of pedestrian detection and tracking research methodology and their constraints are discussed briefly.Most of the camera applications mainly concentrate on picture visibility and tracking.If the pedestrian detection application makes as inbuilt technique, then automatically so many accidents especially of children can be avoided.This pedestrian application mainly used to track the pedestrian movements while he or she is moving on heavy traffic roads and highways or while taking vehicle reverse by using cameras which were fixed on vehicles and make alerts.Such that camera can get more extracted features and helps the future applications.In this research paper a brief literature review is placed according to various researchers along with their techniques.And also compare the performance measures such as accuracy, sensitivity, false alarm rate and detection rate.These experimental results are out performance the methodology and differentiated with present technology.