Masterclass: Computer Vision, OpenCV & Real-Time Video Analytics with Deep Learning

Master real-time computer vision, OpenCV 5, YOLOv10/v11 object detection, Vision Transformers, NVIDIA TensorRT edge acceleration, and video stream analytics.

12 Weeks
Online / Offline
English & Tamil
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Course Overview

Computer Vision is the sensory foundation of artificial intelligence. From autonomous vehicles and surgical robotics to industrial automated optical inspection (AOI) and smart city surveillance, enabling machines to see, analyze, and act in real time is one of the highest-value frontiers in technology. This masterclass bridges classical image processing and cutting-edge deep learning. You will master OpenCV 5, train modern object detection and segmentation models (YOLOv10, YOLO11, and Mask R-CNN) using PyTorch, implement high-speed multi-object tracking with ByteTrack, and optimize vision pipelines for real-time edge execution (30+ FPS) on NVIDIA Jetson using TensorRT and ONNX Runtime.

Skills You Will Master

OpenCV 5 & Image Processing PyTorch Computer Vision YOLOv10 & YOLO11 Architectures Real-Time Object Detection Instance & Semantic Segmentation Multi-Object Tracking (ByteTrack) Vision Transformers (ViT) NVIDIA TensorRT & ONNX Edge AI on NVIDIA Jetson Video Streaming & RTSP Pipelines

Course Curriculum

1. Classical Computer Vision & Image Processing with OpenCV

  • Pixel manipulation, colorspaces (BGR, HSV, LAB), histogram equalization, and adaptive thresholding
  • Spatial filtering: Gaussian blur, bilateral filter, Sobel edge detection, and Canny algorithm
  • Morphological operations (dilation, erosion, opening, closing) and contour analysis
  • Feature detection and matching: Harris corners, SIFT, ORB, and homography image stitching
  • Geometric transformations, camera calibration, perspective rectification, and optical flow

2. Deep Learning for Vision & Convolutional Neural Networks (CNNs)

  • CNN mechanics: Convolutions, pooling, receptive fields, stride, padding, and backpropagation in PyTorch
  • Modern backbone architectures: ResNet, ConvNeXt, EfficientNet, and MobileNetV4
  • Data augmentation pipelines with Albumentations: Random cropping, affine transforms, color jitter, and CutMix/MixUp
  • Transfer learning, fine-tuning pretrained weights, and loss functions (Cross-Entropy, Focal Loss)
  • Visualizing activations with Grad-CAM and debugging feature map representations

3. State-of-the-Art Object Detection & Real-Time YOLO

  • Evolution of object detection: Two-stage (Faster R-CNN) vs One-stage (SSD, RetinaNet, YOLO)
  • Deep dive into modern YOLO architectures (YOLOv8, YOLOv10, and YOLO11): NMS-free dual label assignments, C2f modules, and SPPF
  • Bounding box regression loss: IoU, GIoU, DIoU, CIoU, and Distribution Focal Loss (DFL)
  • Custom dataset curation: Annotation formats (COCO, YOLO, VOC) and Roboflow dataset pipelines
  • Model training, hyperparameter evolution, validation metrics (mAP@0.5, mAP@0.5:0.95), and FPS benchmarking

4. Image Segmentation, Multi-Object Tracking & Pose Estimation

  • Semantic segmentation with U-Net and DeepLabV3+ for pixel-level classification
  • Instance segmentation with YOLO-seg and Mask R-CNN: Polygon mask extraction and overlay
  • Multi-Object Tracking (MOT): Kalman Filters, Hungarian algorithm, SORT, DeepSORT, and ByteTrack
  • Real-time Human Pose Estimation (YOLO-pose): Keypoint extraction, skeletal tracking, and gesture analysis
  • Face detection and biometric recognition with RetinaFace and ArcFace embeddings

5. Vision Transformers (ViT) & Multimodal Vision-Language Models

  • Self-attention mechanisms in computer vision: Patch embeddings, positional encodings, and Vision Transformers (ViT)
  • Swin Transformer and hierarchical feature representation
  • Zero-shot visual recognition with OpenAI CLIP (Contrastive Language-Image Pre-training)
  • Foundation vision models: Segment Anything Model (SAM / FastSAM) for zero-shot mask segmentation
  • Vision-Language Models (VLMs) and Visual Question Answering (VQA) with LLaVA

6. Edge AI Deployment, TensorRT Optimization & Industrial Capstone

  • RTSP video ingestion pipelines with OpenCV, FFmpeg, and GStreamer for multi-camera feeds
  • Model export and cross-platform inference with ONNX Runtime and OpenVINO
  • High-speed GPU acceleration with NVIDIA TensorRT: Engine compilation, FP16 half-precision, and INT8 calibration
  • Edge deployment on NVIDIA Jetson Orin Nano / Orin AGX and Raspberry Pi AI Kit
  • Capstone Project: End-to-End Industrial Visual Inspection & Worker Safety System with Real-Time RTSP Stream Analytics

Local Training Campuses & Placement Drives

Classroom & hybrid batches in Coimbatore & Chennai

Coimbatore • Training

Computer Vision, OpenCV & Real-Time Video Analytics with Deep Learning
Chennai • Training

Computer Vision, OpenCV & Real-Time Video Analytics with Deep Learning

Recommended Career Reading:

Computer Vision Engineer Roadmap 2026: Master OpenCV, YOLO & Edge AI for High-Paying Jobs

Take the Next Step in Your Career

Join our comprehensive Masterclass: Computer Vision, OpenCV & Real-Time Video Analytics with Deep Learning program and gain real-world skills that employers demand.

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