Real Time Object Detection Using Open CV

This project focuses on implementing real-time object detection using Raspberry Pi and OpenCV. Real-time object detection is a critical aspect of computer vision applications, allowing systems to identify and locate objects within a live video stream instantly.

1. PROJECT- 1 EC- 711 REAL TIME OBJECT DETECTION USING RASPBERRY PI & OPEN CV JAWAHARLAL NEHRU GOVERNMENT ENGINEERING COLLEGE, SUNDERNAGAR DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING Submitted By: Khem Singh (20010104029) Reva (20010104044) Project guide- Asst. Prof. Pooja Sharma

2. INTRODUCTION This project focuses on real-time object detection, a crucial component of computer vision applications. It involves identifying and locating objects within a video stream in real-time. Real-time object detection has diverse applications ranging from security and surveillance to automation and robotics. Raspberry Pi serves as the hardware backbone, providing a cost-effective and versatile platform for deploying our computer vision solution.

3. OBJECTIVE Achieve accurate and swift identification of objects within a live video stream. Prioritize the real-time aspect, ensuring minimal latency between object detection and system response. Enhance user experience through intelligent automation. Monitor and secure areas in real-time, alerting to potential threats or unusual activities.

4. SYSTEM OVERVIEW Raspberry Pi: The central computing unit that serves as the brains of the system. Executes the real-time object detection algorithm, manages system resources, and facilitates communication between components. Camera: Captures live video feed, providing input for real-time object detection. Essential for gathering visual data from the environment .

5. SYSTEM OVERVIEW OpenCV Library: Provides a robust set of tools and functions for image and video processing. Implemented in Python code running on Raspberry Pi, enabling seamless interaction with the camera feed.

6. SYSTEM OVERVIEW Pre-trained Model: Utilizes a pre-trained deep learning model for object detection . SSD (Single Shot Multibox Detector) with MobileNetV3 architecture .

7. OVERVIEW OF DATASET Data set used- “ COCO (Common Objects in Context) ". The COCO dataset is a large-scale object detection, segmentation, and captioning dataset It includes 91 object categories, providing a diverse and challenging set of images. Some common object labels in the COCO dataset include person, bicycle, car, airplane, bus, train, truck, bird, cat, dog, horse, etc.

8. W ORKING

9. RESULTS

10. THANKYOU