Computer Vision Analytics
Advanced computer vision system for object detection and image analysis using deep learning
Project Overview
The Computer Vision Analytics platform is a comprehensive solution for real-time image and video analysis using state-of-the-art deep learning models. Built for applications requiring high accuracy and low latency, it provides robust object detection, face recognition, and video analytics capabilities.
The system leverages TensorFlow and PyTorch for model training, OpenCV for image processing, and CUDA for GPU acceleration. It's designed to run both in the cloud and on edge devices, with optimized models for mobile deployment. The platform includes REST APIs, SDKs for multiple platforms, and comprehensive monitoring tools.
Key achievements include 96.8% detection accuracy, real-time processing at 30 FPS, support for 1000+ object classes, and deployment on resource-constrained edge devices.
Project Details
Technologies
Key Features
Object Detection
Real-time detection and classification of objects in images and video streams
Face Recognition
Advanced facial recognition with emotion detection and identity verification
GPU Acceleration
Optimized performance using CUDA and GPU-accelerated deep learning models
Mobile Integration
Mobile SDK for real-time computer vision on iOS and Android devices
Video Analytics
Real-time video processing with object tracking and behavior analysis
Privacy-First
On-device processing with data privacy and security compliance
Challenges & Solutions
Real-time Performance
Achieving sub-second inference times for complex computer vision tasks
Solution:
Optimized model architecture with TensorRT and GPU acceleration
Model Accuracy
Maintaining high accuracy across diverse lighting conditions and environments
Solution:
Implemented data augmentation and transfer learning with custom datasets
Scalability
Handling multiple concurrent video streams and processing requests
Solution:
Built microservices architecture with load balancing and auto-scaling
Edge Deployment
Deploying models on resource-constrained edge devices
Solution:
Model quantization and optimization for mobile and IoT devices
Performance Metrics
Interested in this project?