# System Architecture Documentation ## System Design Overview The 8K Motion Tracking and Voxel Processing System is designed as a distributed, multi-layer architecture optimized for real-time processing of high-resolution multi-modal sensor data. ### Design Principles 1. **Modularity**: Each component is independently testable and replaceable 2. **Scalability**: Horizontal scaling across multiple GPU nodes 3. **Fault Tolerance**: Automatic failover and recovery mechanisms 4. **Performance**: CUDA acceleration and zero-copy data transfers 5. **Extensibility**: Plugin architecture for new sensor types and algorithms --- ## Component Interactions ### System Layers ``` ┌────────────────────────────────────────────────────────────────────────┐ │ Application Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Tracking │ │ Detection │ │ 3D │ │ │ │ Service │ │ Service │ │ Rendering │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └────────────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────────────┐ │ Processing Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Fusion │ │ Voxel │ │ Detection │ │ │ │ Manager │ │ Grid │ │ Tracker │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └────────────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────────────┐ │ Distributed Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Task │ │ Load │ │ Fault │ │ │ │ Scheduler │ │ Balancer │ │ Tolerance │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └────────────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────────────┐ │ Data Pipeline Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Ring Buffers │ │ Shared Memory│ │ Network │ │ │ │ (Lock-free) │ │ (Zero-copy) │ │ (RDMA) │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └────────────────────────────────────────────────────────────────────────┘ │ ▼ ┌────────────────────────────────────────────────────────────────────────┐ │ Hardware Layer │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Cameras │ │ GPUs │ │ Network │ │ │ │ (GigE/USB3) │ │ (CUDA) │ │ (10GbE/IB) │ │ │ └──────────────┘ └──────────────┘ └──────────────┘ │ └────────────────────────────────────────────────────────────────────────┘ ``` --- ## Detailed Component Architecture ### 1. Camera Management System **Purpose**: Manages 10 camera pairs (20 cameras total) with synchronized acquisition. **Components**: ``` CameraManager ├── CameraInterface (x20) │ ├── Connection Management (GigE Vision) │ ├── Configuration (Resolution, FPS, Exposure) │ ├── Frame Acquisition │ └── Health Monitoring ├── CameraPair (x10) │ ├── Stereo Calibration │ ├── Frame Synchronization │ └── Registration Parameters └── Health Monitor ├── FPS Tracking ├── Temperature Monitoring ├── Packet Loss Detection └── Error Recovery ``` **Interaction Flow**: 1. **Initialization**: Connect to cameras via GigE Vision protocol 2. **Configuration**: Set resolution (7680x4320), frame rate (30 FPS), trigger mode 3. **Acquisition**: Hardware-triggered synchronized frame capture 4. **Monitoring**: Continuous health checks (FPS, temperature, packet loss) 5. **Recovery**: Automatic reconnection on failure **Performance Characteristics**: - Connection time: <2 seconds per camera - Synchronization accuracy: <1ms between camera pairs - Health check frequency: 1 Hz - Maximum packet loss tolerance: 0.1% --- ### 2. Video Processing Pipeline **Purpose**: Decode and extract motion from 8K video streams in real-time. **Architecture**: ``` VideoProcessor ├── Decoder Thread │ ├── Hardware Decoder (NVDEC/QSV) │ ├── Codec Handler (HEVC, H.264) │ └── Frame Buffer (Ring Buffer) ├── Motion Extractor (C++) │ ├── Background Subtraction │ ├── Connected Components │ ├── Centroid Calculation │ └── Velocity Estimation └── Synchronization Manager ├── Multi-stream Sync ├── Timestamp Alignment └── Frame Dropping (if needed) ``` **Data Flow**: ``` [Video File/Stream] │ ▼ [Hardware Decoder] ──────────> [Decoded Frame Buffer] │ (HEVC/H.264) │ │ 5-8ms │ ▼ ▼ [Preprocessing] ──────────> [Motion Extractor (C++)] │ (Resize/Convert) │ (OpenMP Parallel) │ 2-3ms │ 12-18ms │ │ ▼ ▼ [Frame Metadata] <───────── [Motion Data Output] │ ├── Coordinates ├── Bounding Boxes ├── Velocities └── Confidence ``` **Optimization Techniques**: - Hardware-accelerated decoding (NVDEC) - Multi-threaded motion extraction (OpenMP) - SIMD instructions for pixel operations - Lock-free ring buffers for thread communication **Performance**: - Decode throughput: 60+ FPS (hardware) vs 15-20 FPS (software) - Motion extraction: 35+ FPS for 8K frames - Memory usage: ~500MB per stream --- ### 3. Fusion System **Purpose**: Combine thermal and monochrome data for enhanced target detection. **Architecture**: ``` FusionManager ├── Registration Engine │ ├── Feature Detection (SIFT/ORB) │ ├── Homography Estimation (RANSAC) │ ├── Image Warping (OpenCV/CUDA) │ └── Quality Metrics ├── Multi-Spectral Detector │ ├── Thermal Detection │ ├── Monochrome Detection │ ├── Confidence Fusion │ └── Cross-Validation ├── False Positive Reducer │ ├── Signature Verification │ ├── Spatial Consistency │ └── Temporal Tracking └── Worker Thread Pool ├── Task Queue ├── Result Queue └── Load Balancing ``` **Fusion Algorithm**: ```python # Pseudo-code for fusion process def fuse_frame_pair(thermal_frame, mono_frame): # Step 1: Update registration if needed if needs_registration_update(): reg_params = estimate_homography(thermal_frame, mono_frame) # Step 2: Align images aligned_thermal = warp_image(thermal_frame, reg_params) # Step 3: Detect in both modalities thermal_detections = detect_thermal(aligned_thermal) mono_detections = detect_mono(mono_frame) # Step 4: Fuse detections fused_detections = [] for t_det in thermal_detections: for m_det in mono_detections: if spatial_overlap(t_det, m_det) > threshold: confidence = fusion_confidence(t_det, m_det) if confidence > min_confidence: fused_detections.append( FusedDetection(t_det, m_det, confidence) ) # Step 5: Cross-validate to remove false positives validated = cross_validate(fused_detections, thermal_frame, mono_frame) # Step 6: Update tracks tracked = update_tracks(validated) return tracked ``` **Performance Characteristics**: - Registration update: 1 Hz (or when quality degrades) - Registration accuracy: <2 pixel RMSE - False positive reduction: 40-60% improvement - Processing time: 8-12ms per frame pair - Target confirmation rate: 85-95% --- ### 4. Distributed Processing System **Purpose**: Coordinate task distribution across multiple GPU nodes. **Architecture**: ``` DistributedProcessor ├── Cluster Manager │ ├── Node Discovery (UDP Broadcast) │ ├── Resource Tracking (GPU, CPU, Memory) │ ├── Topology Optimization (Floyd-Warshall) │ └── Heartbeat System (1 Hz) ├── Task Scheduler │ ├── Priority Queue │ ├── Dependency Resolution │ ├── Task Registry │ └── Completion Tracking ├── Load Balancer │ ├── Worker Selection (Weighted) │ ├── Load Monitoring │ ├── Performance Tracking │ └── Rebalancing Logic ├── Worker Manager │ ├── Worker Thread Pool │ ├── GPU Assignment │ ├── Task Execution │ └── Result Collection └── Fault Tolerance ├── Failure Detection (Heartbeat Timeout) ├── Task Reassignment ├── Worker Recovery └── Failover Metrics ``` **Task Scheduling Algorithm**: ```python # Weighted load balancing def select_worker(available_workers, task): scores = [] for worker in available_workers: # Current load factor (0.0 = idle, 1.0 = busy) load = worker_loads[worker.id] # Performance factor (based on historical execution time) perf = 1.0 / max(avg_execution_time[worker.id], 0.1) # Task priority factor priority = task.priority / 10.0 # Combined score (lower is better) score = load - perf + priority scores.append((score, worker)) # Select worker with lowest score return min(scores, key=lambda x: x[0])[1] ``` **Communication Patterns**: 1. **Master-Worker**: Task assignment and result collection 2. **Peer-to-Peer**: Direct data transfer between nodes (RDMA) 3. **Broadcast**: Cluster-wide status updates 4. **Heartbeat**: Node health monitoring **Performance**: - Node discovery: <2 seconds - Task assignment latency: <1ms - Failover time: <5 seconds - Load imbalance detection: 5 second intervals - Support for 4-16 GPU nodes --- ### 5. Data Pipeline **Purpose**: High-throughput, low-latency data transfer with zero-copy optimizations. **Architecture**: ``` DataPipeline ├── Ring Buffers (per camera) │ ├── Lock-free Implementation │ ├── Multi-producer Support │ ├── Multi-consumer Support │ └── Configurable Size (default: 60 frames) ├── Shared Memory Manager │ ├── mmap-based Allocation │ ├── IPC Support (POSIX) │ ├── Zero-copy Transfers │ └── Memory Pool └── Network Transport ├── RDMA Support (InfiniBand) ├── Zero-copy Send/Receive ├── Scatter-Gather I/O └── Fallback to TCP/IP ``` **Memory Layout**: ``` Shared Memory Segment (per camera) ┌────────────────────────────────────────────────────────────┐ │ Header (64 bytes) │ │ ├── Version │ │ ├── Buffer Size │ │ ├── Frame Width/Height │ │ └── Metadata Offset │ ├────────────────────────────────────────────────────────────┤ │ Frame Buffer 0 (7680 x 4320 = 33.2 MB) │ ├────────────────────────────────────────────────────────────┤ │ Frame Buffer 1 (33.2 MB) │ ├────────────────────────────────────────────────────────────┤ │ ... │ ├────────────────────────────────────────────────────────────┤ │ Frame Buffer N (33.2 MB) │ ├────────────────────────────────────────────────────────────┤ │ Metadata Array │ │ ├── Frame 0 Metadata (timestamp, frame_id, etc.) │ │ ├── Frame 1 Metadata │ │ └── ... │ └────────────────────────────────────────────────────────────┘ ``` **Lock-free Ring Buffer Algorithm**: ```cpp // Simplified lock-free ring buffer class LockFreeRingBuffer { std::atomic write_index_{0}; std::atomic read_index_{0}; size_t capacity_; bool push(const Frame& frame) { uint64_t current_write = write_index_.load(std::memory_order_relaxed); uint64_t next_write = (current_write + 1) % capacity_; uint64_t current_read = read_index_.load(std::memory_order_acquire); // Check if buffer is full if (next_write == current_read) { return false; // Buffer full } // Write data buffer_[current_write] = frame; // Update write index write_index_.store(next_write, std::memory_order_release); return true; } bool pop(Frame& frame) { uint64_t current_read = read_index_.load(std::memory_order_relaxed); uint64_t current_write = write_index_.load(std::memory_order_acquire); // Check if buffer is empty if (current_read == current_write) { return false; // Buffer empty } // Read data frame = buffer_[current_read]; // Update read index uint64_t next_read = (current_read + 1) % capacity_; read_index_.store(next_read, std::memory_order_release); return true; } }; ``` **Performance Characteristics**: - Write throughput: 2.5+ GB/s per camera - Read throughput: 2.0+ GB/s - Latency: <100 microseconds (local), <5ms (network with RDMA) - Zero-copy efficiency: 95%+ (eliminates memory copies) - Scalability: Supports 10-100 cameras per node --- ### 6. Voxel Reconstruction System **Purpose**: Project motion coordinates into 3D voxel space for spatial tracking. **Architecture**: ``` VoxelGrid (CUDA Accelerated) ├── Sparse Voxel Storage │ ├── Hash Table (GPU) │ ├── Octree Structure │ ├── Voxel Activation │ └── Memory Management ├── Projection Engine │ ├── Camera Model (Pinhole) │ ├── Ray Casting (CUDA Kernels) │ ├── Voxel Update (Atomic Ops) │ └── Confidence Weighting └── Optimization ├── Spatial Hashing ├── Parallel Reduction ├── Coalesced Memory Access └── Shared Memory Caching ``` **CUDA Kernel Architecture**: ```cuda // Simplified voxel projection kernel __global__ void project_to_voxel_kernel( const float* __restrict__ coords, // 2D coordinates const float* __restrict__ camera_pose, // Camera position/orientation VoxelGrid* grid, // Sparse voxel grid int num_points ) { int idx = blockIdx.x * blockDim.x + threadIdx.x; if (idx >= num_points) return; // Load 2D coordinate float2 pixel = make_float2(coords[idx*2], coords[idx*2+1]); // Unproject to 3D ray float3 ray_dir = unproject(pixel, camera_pose); // Ray-march through voxel grid float3 pos = camera_pose.position; float step = grid->voxel_size; for (float t = 0; t < max_distance; t += step) { float3 voxel_pos = pos + ray_dir * t; // Compute voxel index int3 voxel_idx = world_to_voxel(voxel_pos, grid); // Atomically update voxel atomicAdd(&grid->data[hash(voxel_idx)], 1.0f); } } ``` **Performance**: - Voxel update rate: 30 FPS for 10,000 points - Memory usage: Sparse storage (~10% of dense grid) - GPU utilization: 30-40% - Ray casting: 1M rays/second --- ## Data Flow Diagrams ### End-to-End Pipeline ``` ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ Camera │────>│ Video │────>│ Motion │────>│ Fusion │ │ Capture │ │ Decode │ │ Extract │ │ Process │ │ (0ms) │ │ (5-8ms) │ │ (12-18ms)│ │ (8-12ms) │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │ ▼ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ Output │<────│ Voxel │<────│ Distrib │<────│ Detection│ │ (Display)│ │ Grid │ │ Process │ │ Tracking │ │ │ │ (5-8ms) │ │ (2-5ms) │ │ (3-5ms) │ └──────────┘ └──────────┘ └──────────┘ └──────────┘ Total Latency: ~35-56ms (excluding camera capture) Target: <33ms for 30 FPS ``` ### Distributed Processing Flow ``` Master Node Worker Node 1 Worker Node 2 │ │ │ │ [Task Assignment] │ │ ├──────────────────────────────>│ │ │ │ │ │ [GPU Process] │ │ │ │ │ [Result Collection] │ │ │<──────────────────────────────┤ │ │ │ │ │ [Task Assignment] │ │ ├────────────────────────────────────────────────────────>│ │ │ │ │ │ [GPU Process] │ │ │ │ [Result Collection] │ │ │<────────────────────────────────────────────────────────┤ │ │ │ │ [Heartbeat] │ │ │<──────────────────────────────┤ │ │<────────────────────────────────────────────────────────┤ │ │ │ ``` --- ## Performance Characteristics ### Throughput Analysis | Component | Sequential | Parallel (4 threads) | GPU | |-----------|------------|---------------------|-----| | 8K Decode | 15-20 FPS | 60+ FPS (HW) | N/A | | Motion Extract | 8-10 FPS | 35+ FPS | N/A | | Fusion | 12-15 FPS | 30+ FPS | 50+ FPS | | Voxel Project | 5-8 FPS | 15-20 FPS | 30+ FPS | ### Latency Breakdown ``` Frame Pipeline (Target: <33ms for 30 FPS) ───────────────────────────────────────────────────────── Video Decode ████░░░░░░░░░░░░░░░░░░░░░ 5-8ms Motion Extract ████████████░░░░░░░░░░░░░ 12-18ms Fusion Process ████████░░░░░░░░░░░░░░░░░ 8-12ms Detection Track ███░░░░░░░░░░░░░░░░░░░░░░ 3-5ms Voxel Project ██████░░░░░░░░░░░░░░░░░░░ 5-8ms Distributed ██░░░░░░░░░░░░░░░░░░░░░░░ 2-5ms ───────────────────────────────────────────────────────── Total ██████████████████████████ 35-56ms Optimization needed to meet <33ms target: - Parallel fusion processing - Async voxel updates - Pipeline overlapping ``` ### Scalability **Horizontal Scaling** (Adding more nodes): - 1 Node: 2 camera pairs (4 cameras) - 2 Nodes: 5 camera pairs (10 cameras) - 4 Nodes: 10 camera pairs (20 cameras) - 8 Nodes: 20 camera pairs (40 cameras) **Vertical Scaling** (More GPUs per node): - 1 GPU: 1-2 camera pairs - 2 GPUs: 3-4 camera pairs - 4 GPUs: 5-8 camera pairs --- ## Scalability Considerations ### Design for Scale 1. **Stateless Workers**: Workers don't maintain state between tasks 2. **Data Locality**: Tasks assigned to nodes with required data 3. **Load Balancing**: Dynamic task distribution based on worker load 4. **Fault Isolation**: Node failures don't affect other nodes 5. **Resource Pools**: Pre-allocated GPU memory and thread pools ### Bottlenecks and Solutions | Bottleneck | Impact | Solution | |------------|--------|----------| | Network Bandwidth | Data transfer delays | RDMA, compression, local processing | | GPU Memory | Limited camera pairs/node | Sparse data structures, streaming | | CPU-GPU Transfer | PCIe bottleneck | Pinned memory, async transfers | | Synchronization | Lock contention | Lock-free data structures | | Task Scheduling | Load imbalance | Weighted scheduling, work stealing | ### Future Expansion - **More Cameras**: Add nodes, scale horizontally - **Higher Resolution**: Upgrade GPUs, optimize CUDA kernels - **More Modalities**: Extend fusion system, add sensor interfaces - **Lower Latency**: Optimize pipeline, reduce buffering - **Cloud Deployment**: Add network optimization, edge computing --- ## Design Patterns ### 1. Producer-Consumer Pattern - Cameras produce frames → Pipeline consumes - Lock-free ring buffers for thread-safe communication ### 2. Pipeline Pattern - Sequential stages with data flow - Each stage can be parallelized independently ### 3. Master-Worker Pattern - Master coordinates, workers execute - Dynamic task distribution ### 4. Observer Pattern - Callbacks for motion detection, errors, status updates - Decouples components ### 5. Factory Pattern - Camera creation based on type (Mono/Thermal, GigE/USB) - Codec selection based on format --- ## Technology Stack ### Languages - **Python 3.8+**: Application logic, data pipeline - **C++17**: Performance-critical components (motion extraction, fusion) - **CUDA**: GPU-accelerated kernels (voxel processing, detection) ### Libraries - **OpenCV 4.5+**: Image processing, calibration - **NumPy**: Array operations - **PyBind11**: C++/Python bindings - **Protocol Buffers**: Serialization - **ZeroMQ**: Network messaging - **RDMA**: High-speed network transfers (optional) ### Hardware Requirements - **GPU**: NVIDIA RTX 3090/4090 with CUDA 11.0+ - **Network**: 10GbE or InfiniBand for multi-node - **Cameras**: GigE Vision compatible --- ## Security Considerations - Camera access control (IP filtering, authentication) - Encrypted network communication (TLS/SSL) - Secure calibration data storage - Input validation for all external data - Resource limits to prevent DoS --- ## References - [CUDA Programming Guide](https://docs.nvidia.com/cuda/) - [GigE Vision Standard](https://www.automate.org/vision/gige-vision) - [Lock-free Programming](https://preshing.com/20120612/an-introduction-to-lock-free-programming/) - [RDMA Programming](https://www.rdmamojo.com/)