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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Nasreen, Azra
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Computer Science & IT Electrical & Electronics Engineering

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Enhancing automatic license plate recognition in Indian scenarios Samaga, Abhinav; Lobo, Allen Joel; Nasreen, Azra; Pattar, Ramakanth Kumar; Trivedi, Neeta; Raj, Peehu; Sreelakshmi, Koratagere
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp365-373

Abstract

Automatic license plate recognition (ALPR) technology has gained widespread use in many countries, including India. With the explosion in the number of vehicles plying over the roads in the past few years, automating the process of documenting vehicle license plates for use by law enforcement agencies and traffic management authorities has great significance. There have been various advancements in the object detection, object tracking, and optical character recognition domain but integrated pipelines for ALPR in Indian scenarios are a rare occurrence. This paper proposes an architecture that can track vehicles across multiple frames, detect number plates and perform optical character recognition (OCR) on them. A dataset consisting of Indian vehicles for the detection of oblique license plates is collected and a framework to increase the accuracy of OCR using the data across multiple frames is proposed. The proposed system can record license plate readings of vehicles averaging 527.99 and 2157.09 ms per frame using graphics processing unit (GPU) and central processing unit (CPU) respectively.
Multi-camera multi-person tracking with DeepSORT and MySQL Raghavendra, Shashank Horakodige; Sorapalli, Yashasvi; Poojar S. V., Nehashri; Maddirala, Hrithik; Kumar P., Ramakanth; Nasreen, Azra; Trivedi, Neeta; Agarwal, Ashish; K., Sreelakshmi
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp997-1009

Abstract

Multi-camera multi-object tracking refers to the process of simultaneously tracking numerous objects using a network of connected cameras. Constructing an accurate depiction of an object’s movements requires the analysis of video data from many camera feeds, detection of items of interest, and their association across various camera perspectives. The objective is to accurately estimate the trajectories of the objects as they navigate through a monitored area. It has several uses, including surveillance, robotics, self-driving cars, and augmented reality. The current version of an object tracking algorithm, DeepSORT, doesn’t account for errors caused by occlusion or implementation of multiple cameras. In this paper, DeepSORT has been extended by introducing new states to improve the tracking performance in scenarios where objects are occluded in the presence of multiple cameras. The communication of track information across multiple cameras is achieved with the help of a database. The suggested system performs better in situations where objects are occluded, whether due to object occlusions or person occlusions.
Co-Authors Agarwal, Ashish K., Sreelakshmi Kumar P., Ramakanth Lobo, Allen Joel Maddirala, Hrithik Pattar, Ramakanth Kumar Poojar S. V., Nehashri Raghavendra, Shashank Horakodige Raj, Peehu Samaga, Abhinav Sorapalli, Yashasvi Sreelakshmi, Koratagere Trivedi, Neeta