Cars and Pedestrians Detection and Tracking: Using Haar Cascade Classifiers

Registration ID: IJNRD_188283

Published ID: IJNRD2303053

DOI: Click Here to Get

SWATHI BHASKARAN , KODA INDU , TANGI ALEKHYA , TANGULA KAVERI , VARIGETI JAYA SRUTHI

Object detection, OpenCV, Haar Cascade, Car and Pedestrians, Tracking, Python

Computer vision finds an important application in traffic surveillance, management and monitoring. The aim of this paper is to review implementation of Haar Cascade classifiers in detecting cars and pedestrians from either a video input or a live stream input from surveillance cameras. The video stream is broken into frames. Each frame is taken up as an image to detect cars and pedestrians. This is done by using sliding window approach. Depending on where the window is currently positioned, each stage of the classifier marks a specific area as positive or negative. We use Computer Vision Library (OpenCV). The frames with the object detected, put together gives an efficient tracking of the same. This study also evaluates Haar Cascade method with respect to other object detection algorithms. The paper concludes with a discussion on the future scope of this work.

Published Paper   E-Certificate

SWATHI BHASKARAN, KODA INDU, TANGI ALEKHYA, TANGULA KAVERI, & VARIGETI JAYA SRUTHI (March-2023). Cars and Pedestrians Detection and Tracking: Using Haar Cascade Classifiers. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), a495-a499. https://ijnrd.org/papers/IJNRD2303053.pdf

Volume 8 Issue 3, March-2023

Pages : a495-a499

Paper Reg. ID: IJNRD_188283

Published Paper Id: IJNRD2303053

Downloads: 000121977

Research Area: Computer Science & Technology 

Country: Visakhapatnam, Andhra Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303053.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303053

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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