Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
SlideShare a Scribd company logo

Real Time Object Detection Using Open CV

•Download as PPTX, PDF•
•
K
Khem

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 of 10
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
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.
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.
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 .
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.
SYSTEM OVERVIEW Pre-trained Model: ● Utilizes a pre-trained deep learning model for object detection . ● SSD (Single Shot Multibox Detector) with MobileNetV3 architecture .
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.
WORKING
RESULTS
THANKYOU

More Related Content

Real Time Object Detection Using Open CV

  • 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.