Perception-RT

A high-performance real-time video processing system designed for intelligent vehicle analytics and computer vision applications. Built with CUDA acceleration, TensorRT optimization, and comprehensive performance monitoring.

Key Features

Real-Time Performance

• Sub-3ms Latency: Average end-to-end processing latency of 2.07ms with 95th percentile at 3.11ms
• 100% Deadline Compliance: Maintains 30 FPS target with zero missed deadlines
• CUDA Acceleration: GPU-optimized motion detection and preprocessing pipelines
• TensorRT Integration: Optimized FP16 inference with dynamic batching support

Computer Vision Capabilities

• YOLOv11 Object Detection: Real-time vehicle detection with 10+ objects per frame
• Multi-Object Tracking: Persistent vehicle tracking with trajectory analysis
• Optical Flow Analysis: Lucas-Kanade sparse optical flow for motion estimation

Vehicle Analytics

• Collision Warning System: Real-time proximity detection and safety zone monitoring
• Traffic Density Analysis: Automated vehicle counting and density metrics
• Safety Zone Monitoring: Configurable danger zones with real-time alerts

Production-Ready Architecture

• Modular Pipeline: Decoupled processing stages with configurable feature toggles
• Comprehensive Monitoring: Real-time performance metrics and telemetry
• Memory Management: Efficient buffering with configurable memory limits
• Configuration-Driven: YAML-based configuration for deployment flexibility

Performance Metrics

The system demonstrates exceptional efficiency with inference operations comprising only 14.9% of total processing time, leaving substantial headroom for additional features and higher resolution inputs.

Consistent 100% deadline compliance ensures reliable real-time operation essential for safety-critical applications.

Advanced vehicle analytics with multi-object tracking, detecting an average of 1.3 vehicles per frame with up to 6 simultaneous tracked objects.

Technical Architecture

Core Components

• Pipeline Controller: Adaptive quality control with hysteresis-based switching
• ML Engine: TensorRT-accelerated inference with OpenCV DNN fallback
• GPU Motion Detector: CUDA kernels for background subtraction and frame differencing
• Vehicle Analytics Engine: Specialized module for automotive applications
• Output Manager: Comprehensive logging, video output, and performance monitoring

Dependencies

• CUDA Toolkit
• TensorRT
• OpenCV 4.5+
• YOLOv11
• C++20