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Portable Object Detection in Real-Time

Dr.Kavitha Soppari1 , D Varun2, Eedula Rithvik3, Manchala Anudeep4

Assoc. Professor and Head of the Department of CSE(AI&ML) of ACE Engineering College1

Students of Department CSE(AI&ML) of ACE Engineering College2,3,4

Abstract
Portable Object Detection in Real-Time is a computer vision-based project that enables the identification and classification of objects using a laptop's built-in camera. The system leverages deep learning techniques, specifically convolutional neural networks (CNNs) and pre-trained models such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), to perform efficient and accurate object detection. The project aims to provide a lightweight and portable solution without requiring external hardware, making it accessible for various applications such as security monitoring, automated inventory management, and assistive technologies. The system processes live video feed, detects objects in real time, and displays results dynamically. This approach ensures high-speed performance while maintaining accuracy, making it suitable for real-world deployment in resource-constrained environments.

Keywords: Object Detection, Real-Time Processing, Computer Vision, Deep Learning, Convolutional Neural Networks (CNNs), YOLO, SSD, Machine Learning, Laptop Camera, Portable Solution

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