Cars and Pedestrians Detection and Tracking: Using Haar Cascade Classifiers
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.
"Cars and Pedestrians Detection and Tracking: Using Haar Cascade Classifiers", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.a495-a499, March-2023, Available :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: 000118844
Research Area: Computer Science & Technology
Country: Visakhapatnam, Andhra Pradesh, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
