This bootcamp introduces participants to the development of AI-powered camera detection applications using Python, OpenCV, PyTorch, pretrained YOLO models, and FastAPI. It covers programming foundations, machine learning and deep learning concepts, image and video processing, real-time object detection, and integration with web applications.

Participants progress from foundational concepts to an end-to-end camera detection prototype that connects camera input, AI inference, backend APIs, and a web frontend. Applications include object monitoring, automated inspection, and pest detection where suitable models and labelled datasets are available.

 

 Duration 20 Days – 140 hrs.

 

Phase	Duration
Python Programming for AI	3 days
ML & Deep Learning	3 days
Computer Vision and AI-Powered Camera	5 days
AI Model Deployment, REST APIs, and Web	4 days
Capstone Project	5 days
Total	20 days / 140 hours

 

Objectives

• Develop Python programs for data handling, image processing, and AI application development.
• Explain the fundamentals of machine learning, deep learning, and object detection.
• Process images, recorded videos, and live camera streams using OpenCV.
• Implement object detection using pretrained YOLO models.
• Apply basic object tracking, counting, and event detection.
• Prepare datasets and explain the requirements for fine-tuning detection models.
• Interpret detection metrics and identify model limitations.
• Develop REST APIs that expose AI inference functionality.
• Integrate detection results into a web application.
• Build and document an end-to-end AI-powered camera detection prototype.

 

Target Audience 

• Software developers building AI-enabled applications.
• Python programmers entering computer vision development.
• Web developers integrating AI inference into existing applications.
• Engineers developing camera monitoring and automated inspection solutions.
• Technical teams working on pest monitoring or similar object detection applications.
• AI and machine learning practitioners seeking experience with camera-based systems.

 

Prerequisites 

• Participants should have basic programming knowledge and web development experience.
• The client should identify the intended camera detection use case before the training begins.
• Suitable development machines, cameras, and computing resources must be available.
• Custom model fine-tuning depends on the availability and quality of labelled data.
• The capstone deliverable is a proof of concept; production readiness, accuracy, scalability, and real-time performance depend on the specific requirements and hardware.

 

 

Course Outline

Phase 1: Python Programming Fundamentals for AI – Development (3D, 21 hrs.)

Day 1: Python Programming Essentials

• Python installation and development environment setup
• Variables, data types, operators, and type conversion
• Conditional statements and loops
• Functions, parameters, return values, and scope
• Hands-on programming exercises

Day 2: Python Data Structures and Application Development

• Lists, tuples, dictionaries, and sets
• Strings, comprehensions, and common built-in functions
• Modules, packages, and exception handling
• File handling and JSON processing
• Object-oriented programming fundamentals

 Day 3: Python Libraries for AI Development

• Virtual environments and package management
• NumPy arrays, indexing, slicing, and basic numerical operations
• Introduction to Pandas for data handling
• Installing and configuring OpenCV and PyTorch
• Reading images and videos using Python
• Introduction to AI development workflows

Expected Outcome: Participants can write Python programs, manage the

development environment, and perform basic image and data processing.

 

 

Phase 2: Machine Learning and Deep Learning – Fundamentals (3D, 21 hrs.)

Day 4: AI and Machine Learning Fundamentals

• Artificial Intelligence, Machine Learning, and Deep Learning
• Supervised and unsupervised learning
• Dataset, features, labels, and model predictions
• Classification and regression overview
• AI applications, capabilities, and limitations

 Day 5: Deep Learning and Neural Networks

• Neural network architecture and layers
• Weights, biases, activation functions, and loss functions
• Training, validation, and testing workflows
• CNN fundamentals for image-related tasks
• Pretrained models and transfer learning

Day 6: Model Evaluation and Object Detection Concepts

• Overfitting, underfitting, and generalization
• Confusion matrix, precision, recall, and F1-score
• Image classification versus object detection
• Bounding boxes, confidence scores, and Intersection over Union (IoU)
• Introduction to object detection evaluation metrics
• Overview of pretrained AI models and inference

 Expected Outcome: Participants understand the AI model development lifecycle,

common evaluation metrics, and the principles behind object detection.

 

 

Phase 3: Computer Vision and AI-Powered Camera – Object Detection (5D, 35 hrs.)

Day 7: Computer Vision with OpenCV

• Digital image representation and color spaces
• Image reading, display, resizing, and cropping
• Image filtering and noise reduction
• Thresholding, edge detection, and contours
• Image preprocessing and enhancement

 Day 8: Video Processing and Camera Integration

• Reading and processing video files
• Frame extraction and frame-by-frame processing
• USB webcam integration using OpenCV
• Introduction to IP cameras and RTSP streams
• Camera error handling and frame-rate measurement

 Day 9: YOLO-Based Object Detection

• Object detection pipeline and YOLO overview
• Setting up a pretrained YOLO-family model
• Image and video inference
• Bounding boxes, class labels, and confidence thresholds
• Visualizing and saving detection results

Day 10: Real-Time Detection and Object Tracking

• Live camera-based object detection
• Processing and displaying real-time detection results
• Introduction to object tracking and object IDs
• Basic object counting and event detection
• Measuring inference latency and frames per second
• Handling common detection errors

 Day 11: Custom Detection and Model Evaluation

• Defining target objects and detection requirements
• Dataset collection and annotation fundamentals
• Dataset organization and annotation formats
• Introduction to fine-tuning pretrained YOLO models
• Model validation and evaluation
• Testing detection under different lighting and environmental conditions

Expected Outcome: Participants build a working Python application that detects

selected objects in images, recorded videos, or a supported live camera feed.

Basic model customization is covered where suitable data and computing

resources are available.

 

Phase 4: AI Model Deployment, REST APIs, and Web – Application Integration (4D, 28 hrs.)

Day 12: AI Inference Application Development

• Designing an AI inference pipeline
• Loading and executing pretrained models
• Separating model inference from application logic
• Processing image uploads and video frames
• Logging, configuration, and error handling

 Day 13: Building REST APIs with FastAPI

• REST API fundamentals and FastAPI setup
• Request and response models
• Image upload and inference endpoints
• Returning detection labels, confidence scores, and bounding boxes as JSON
• API validation and interactive documentation

 Day 14: Web Application Integration

• Frontend-to-backend communication
• Calling AI APIs using JavaScript
• Integrating with the client’s existing web framework
• Displaying detection results and annotated images
• Handling API errors and presenting inference status

 Day 15: Camera Streaming and Deployment Fundamentals

• Camera-to-inference-to-frontend architecture
• Streaming and WebSocket concepts
• Basic latency and resource optimization
• Managing concurrent requests
• Deployment configuration, logging, and security fundamentals

Expected Outcome: Participants integrate AI inference into a web application

through APIs and demonstrate detection results through a frontend interface.

 

Phase 5: End-to-End AI Camera Detection Capstone – Project (5D, 35 hrs.)

Day 16: Requirements Analysis and System Design

• Identify the client’s target detection use case
• Define object classes, camera inputs, and success criteria
• Design the system architecture
• Select the model and deployment approach
• Prepare a project plan and test scenarios

 Day 17: Camera and AI Detection Pipeline

• Implement camera or video input
• Integrate the selected detection model
• Process frames and generate detection metadata
• Display bounding boxes and labels
• Test the detection pipeline

 Day 18: Backend and Frontend Integration

• Implement inference API endpoints
• Connect the AI service to the web application
• Display detection results and object information
• Handle errors and camera interruptions
• Validate end-to-end application functionality

Day 19: Testing and Performance Optimization 

• Functional testing and edge-case testing
• Measure inference latency and throughput
• Test different input conditions
• Optimize model settings and frame processing
• Document limitations and outstanding issues

 Day 20: Final Demonstration and Knowledge Transfer

• Demonstrate the completed prototype
• Review the application architecture and source code
• Present test results and performance observations
• Document setup instructions and API usage
• Identify production-readiness gaps and future enhancements

Expected Outcome: A demonstrable proof of concept integrating camera input, AI

object detection, backend inference, and a web frontend, supported by source

code, technical documentation, test results, and a list of recommended next steps.

Recommended Technology Stack

• Programming: Python
• Computer Vision: OpenCV
• Object Detection: Pretrained YOLO-family model
• Deep Learning: PyTorch
• Backend API: FastAPI
• Frontend Integration: JavaScript and the client’s existing web framework
• Optional Deployment: ONNX Runtime or Docker, depending on requirements

Training Methodology

• Instructor-led technical sessions
• Live coding and guided demonstrations
• Hands-on programming exercises
• Image, video, and camera-based experiments
• API development and web integration labs
• Capstone project development and final demonstration

 

Scope note:  This is a training program with a hands-on proof-of-concept deliverable. Production deployment, guaranteed detection accuracy, and integration into a live business system would require separate requirements, validation, and acceptance planning.