https://github.com/Bossthetigan/NOLO/releases

NOLO: Live AI PTZ Camera with Real-Time Object Tracking and Streaming

NOLO stands for Never Only Look Once. It is a live AI powered PTZ camera platform designed to track objects, stream video, and drive PTZ hardware in real time. Built for developers, researchers, and engineers, NOLO blends state-of-the-art computer vision with flexible hardware control to deliver reliable, low-latency tracking and streaming workflows. The project emphasizes clarity, reliability, and extensibility so you can adapt it to security, robotics, research, or content creation.

🧭 Real-time tracking that adapts to motion, occlusion, and changing scenes
🎯 Accurate object detection using modern deep learning models (Yolo and friends)
🎥 Live streaming through standard webcam interfaces and FFmpeg pipelines
🧰 Open, modular architecture with clean interfaces for components like AI, streaming, and PTZ control
🧠 Go-based core with bindings to OpenCV via GoCV for fast, native performance
🖥️ Works across Windows, Linux, and macOS, with clear instructions for embedded targets

This README provides a complete, practical blueprint to understand, install, configure, and operate NOLO. It covers the architecture, components, deployment scenarios, and practical examples to help you get up and running quickly. It also describes how NOLO can be extended with custom models, adapters, or hardware to match your specific needs.

Table of contents

• Quick overview
• Core features
• How NOLO works
• Hardware and software requirements
• Architecture and components
• Getting started: quick setup
• Installation by platform
• Running NOLO locally
• Configuring NOLO
• AI tracking and object detection details
• PTZ control and camera handling
• Webcam streaming and media pipelines
• Sample use cases and workflows
• Performance, tuning, and troubleshooting
• Debugging, logging, and diagnostics
• Extending NOLO: plugins and modules
• Testing and validation
• Deployment patterns
• Development workflow and contribution
• Roadmap and future goals
• License and attribution
• References and resources

Quick overview NOLO targets real-time AI-assisted PTZ control with streaming. The pipeline starts with a video feed from a camera or webcam. A vision module runs an object detector to identify targets of interest. A tracking module estimates motion and predicts trajectories. A PTZ controller adjusts the camera to keep the target centered and framed. A streaming module publishes the video with overlays to local display or network endpoints. All components are designed to be modular, so you can swap detectors, trackers, or streaming backends with minimal changes to the rest of the system.

Core features

• Real-time object detection and tracking: NOLO uses modern, fast detectors such as YOLO variants and lightweight alternatives to balance speed and accuracy in real-world scenes.
• PTZ integration: The PTZ module translates tracking results into smooth pan, tilt, and zoom commands. It supports hardware controllers via serial, network, and GPIO interfaces.
• Live streaming: Output can be streamed to a local window, a network stream, or embedded into custom dashboards. The streaming engine integrates with FFmpeg for robust media handling.
• Go-based core with OpenCV bindings: The core uses Go for performance and simplicity, with GoCV to access OpenCV features for preprocessing and visualization.
• Extensible architecture: Each major subsystem (AI, vision, streaming, PTZ) exposes well-defined interfaces, enabling easy replacement and experimentation.
• Cross-platform support: Targeted for Windows, Linux, and macOS. Embedded and edge deployments are supported with lightweight build configurations.
• Documentation and examples: The repository provides end-to-end examples, sample configs, and runnable tutorials to help you learn quickly.
• Community and ecosystem: The project aligns with common AI, computer vision, and video processing ecosystems and supports integration with external tools like YouTube workflows and other streaming platforms.

How NOLO works NOLO orchestrates several layers to achieve accurate tracking and smooth camera control:

• Input layer: Accepts video sources from USB webcams, IP cameras, or pre-recorded streams. The input layer normalizes frames and timestamps for downstream components.
• Vision layer: Runs object detectors to identify targets of interest. It also handles image preprocessing steps such as resizing, color space conversion, and normalization.
• Tracking layer: Maintains trajectories, estimates velocities, and predicts short-term positions to enable proactive camera motion. It uses a fusion of detection results and motion models to keep the target centered.
• Control layer: Translates tracking data into PTZ commands. It handles calibration data, pan/tilt limits, velocity constraints, and smoothing filters to avoid jitter.
• Streaming layer: Renders overlays, encodes video, and streams to local displays or network endpoints. It leverages FFmpeg or native streaming backends for compatibility.
• Orchestrator: Coordinates all layers with clean lifecycle management, error handling, and metrics collection. It provides configuration reloading and graceful shutdown.

Hardware and software requirements

• Operating system: Windows, Linux (x86_64), macOS. Embedded targets supported with cross-compilation.
• Go toolchain: Go 1.19+ (adjustable per release).
• C/C++ dependencies: OpenCV, FFmpeg, libjpeg, libpng, zlib (or system equivalents), and OpenCV bindings for Go (GoCV).
• Camera hardware: PTZ-enabled cameras supporting pan, tilt, and zoom with a controllable interface (serial, USB, or network).
• PTZ controller compatibility: Serial over USB, SPI, or Ethernet-based control protocols. Pinouts and wiring must match your hardware.
• Optional GPU acceleration: NVIDIA GPUs via CUDA for faster inference. CPU fallback is supported for small-scale setups.
• RAM and CPU: A modest workstation will handle basic tracking; real-time performance improves with more cores and memory.
• Network: If streaming to a remote endpoint, ensure bandwidth and latency are suitable for your application.

Architecture and components NOLO's architecture follows a modular design with clear boundaries between components. This makes it easy to replace models, change streaming backends, or adapt PTZ strategies without rewriting large swathes of code.

• Input Module
  • Supports USB webcams, IP cameras, RTSP streams, and file-based inputs.
  • Provides synchronized frames to the Vision Module.
  • Handles time stamps, frame drops, and jitter reduction.
• Vision Module
  • Detects objects using YOLO-family detectors, lightweight detectors for low-power devices, or custom models.
  • Performs non-maximum suppression, confidence thresholding, and class filtering.
  • Exposes hooks for post-processing to compute centers, bounding boxes, and movement cues.
• Tracking Module
  • Implements a tracker that fuses detections with motion cues to estimate target trajectories.
  • Applies Kalman-like filters or more robust particle/state estimators depending on the environment.
  • Produces pan-tilt commands aligned with the camera’s coordinate system.
• PTZ Control Module
  • Converts tracking output into actionable PTZ commands.
  • Implements acceleration, deceleration, smoothing, and boundary constraints.
  • Provides safe shutdown and calibration routines for new hardware.
• Streaming Module
  • Renders overlays (bounding boxes, labels, trajectories) onto frames.
  • Packages frames into a stream, optionally via RTSP, RTMP, or local display.
  • Integrates with FFmpeg for encoding and transport.
• Configuration and Orchestration
  • Central configuration with defaults and per-environment overrides.
  • Lifecycle management: start, stop, pause, resume, reload config.
  • Observability: basic metrics, logs, and health checks exposed through a simple API.
• Optional Plugins and Extensions
  • Custom detectors
  • Alternative trackers
  • Different streaming backends
  • Additional sensors or control interfaces

Getting started: quick setup This section provides a fast path to a functional NOLO instance on a typical developer machine. The steps emphasize clarity and reproducibility, with concrete commands and explanations.

1. Prerequisites

• Install Go: download and install from the official source for your platform. Verify with go version.
• Install OpenCV and the GoCV bindings. OpenCV 4.x is recommended. Build GoCV against your OpenCV installation to ensure compatibility.
• Install FFmpeg: essential for streaming and encoding. Ensure ffmpeg is in your PATH.
• Ensure a PTZ-capable camera is available. Confirm the control interface (serial, HTTP, or other protocol) and obtain calibration data if needed.
• If you plan to use GPU acceleration, install NVIDIA drivers and CUDA, plus the CUDA-enabled build of the detectors.

2. Quickstart commands

• Clone the repository and navigate to it.
  • git clone --depth 1 https://github.com/Bossthetigan/NOLO.git
  • cd NOLO
• Install dependencies (example for a Linux/macOS environment with Homebrew or apt):
  • sudo apt-get update
  • sudo apt-get install -y build-essential cmake pkg-config libjpeg-dev libpng-dev libtiff-dev libopencv-dev ffmpeg
  • brew install pkg-config opencv ffmpeg
• Build NOLO
  • go mod download
  • go build ./...
• Run a basic example
  • ./nolo run --config configs/default.yaml
  • You can override configuration with environment variables or a custom YAML file.

3. What you’ll get after setup

• A running NOLO instance that captures frames from a camera.
• A detector and tracker working together to identify targets.
• PTZ commands that move the camera to keep the target centered.
• A live overlay on the video stream showing bounding boxes and trajectories.

Installation by platform

• Linux
  • Install dependencies via your package manager.
  • Use AppImage or prebuilt binaries when available. If you build from source, ensure OpenCV libraries are visible to the linker.
  • Run NOLO with a Linux-friendly configuration file.
• Windows
  • Obtain the Windows installer from the Release page and run it.
  • The installer sets up the Go toolchain and dependencies for you or prompts you to install them.
• macOS
  • Use Homebrew to install OpenCV and FFmpeg as dependencies.
  • Build and run NOLO using the same commands as Linux, adjusting for macOS paths and conventions.
• Embedded targets
  • Compile a lean NOLO binary with only the required modules.
  • Use a compact video pipeline and lightweight detector models that fit the device constraints.
  • Ensure you have access to the PTZ controller and the necessary hardware interfaces.

Running NOLO locally

• Start with a baseline config. The default.yaml provides sane defaults for many setups.
• Verify camera initialization
  • Confirm that the input module is properly connecting to your camera.
  • Check frame rate, resolution, and color format.
• Validate detector and tracker
  • Confirm the detector loads correctly and returns bounding boxes with confidence scores.
  • Verify the tracker maintains target identity across frames and produces stable trajectories.
• Test PTZ control
  • Calibrate pan and tilt offsets so that the camera responds predictably to commands.
  • Check limits, speed, and smoothing settings to avoid abrupt motions.
• Observe the streaming path
  • Ensure the video is rendered with overlays and is accessible through your chosen path (local display, RTSP/RTMP server, or file).
• Logging and diagnostics
  • Enable verbose logs during initial runs.
  • Review logs for detector failures, frame drops, or PTZ command anomalies.
• Iterative tuning
  • Experiment with different detectors, confidence thresholds, and NMS settings.
  • Adjust the smoothing parameters in the PTZ controller to achieve stable framing.

Configuring NOLO

• YAML-based configuration
  • The config file controls sources, detectors, trackers, PTZ ranges, and streaming options.
  • Define camera source (device path or RTSP URL), resolution, and frame rate.
  • Choose the detector model, its weights, and the inference backend (CPU or GPU).
  • Set the tracker type, motion model, and prediction window.
  • Provide PTZ calibration data, including pan/tilt ranges, speed, and backlash compensation.
• Environment variables
  • Override key values without editing the YAML.
  • Useful for containerized deployments and CI pipelines.
• Calibration data
  • Camera intrinsics and extrinsics improve pose estimation for safe and accurate PTZ control.
  • Store calibration results in a dedicated config section and load on startup.
• Security considerations
  • If exposing streaming endpoints, ensure authentication and encryption where possible.
  • Keep the device firmware and software up to date.

AI tracking and object detection details

• Detector models
  • YOLO variants provide a practical balance of speed and accuracy for real-time operation.
  • You can select small, fast models for edge devices or larger models for higher precision on capable hardware.
  • Weights and configuration files are loaded at runtime; you can swap models without recompiling.
• Tracking strategies
  • Simple centroid trackers work in clean, stable scenes.
  • Kalman filters improve stability across occlusions.
  • Multi-target tracking handles multiple objects simultaneously, with robust identity management.
• Post-processing
  • Non-maximum suppression is tuned to reduce duplicate detections.
  • Confidence thresholds help filter low-probability detections to reduce noise.
• Overlay rendering
  • Real-time overlays include bounding boxes, class labels, and track IDs.
  • Trajectories are drawn as lightweight polylines to visualize motion over time.
• Performance notes
  • Inference speed and frame rate depend on the detector, image size, and hardware.
  • Edge devices benefit from smaller image sizes and optimized models.
  • Caching, batching, and asynchronous pipelines can smooth processing under load.

PTZ control and camera handling

• Calibrated motion
  • Pan and tilt commands translate to servo or motor positions.
  • The system uses smoothing to prevent jitter and overshoot.
• Command safety
  • Each command is bounded by min/max limits.
  • If the target moves out of trackable range, the system can pause movement to prevent damage.
• Hardware interfaces
  • Serial (UART/USB), TCP/UDP, and HTTP-based control are supported.
  • A hardware abstraction layer keeps the code portable across different camera models.
• Calibration workflow
  • A calibration routine aligns the camera's reported position with the control system.
  • Recalibrate after hardware changes or significant mechanical drift.

Webcam streaming and media pipelines

• Local display
  • A window shows the live feed with overlays for easy monitoring.
• Network streaming
  • RTSP or RTMP streams can be produced using FFmpeg.
  • Streaming parameters like bitrate, resolution, and framerate are configurable.
• Recording
  • Raw or overlayed streams can be saved for offline analysis.
  • Time-stamped clips are organized for quick retrieval.
• YouTube and external services
  • NOLO can be used as a source for live streams that feed into YouTube or other streaming platforms.
  • Authentication and stream keys are managed via the streaming module or a separate launcher script.
• Overlay and UI
  • Real-time overlays assist in debugging and demonstration.
  • Clear labels show class names, confidence scores, and track IDs.

Sample use cases and workflows

• Security and surveillance
  • Use NOLO to track human activity in a monitored space.
  • Configure frames per second to balance CPU usage and response time.
  • Integrate with alerting systems for unusual movement or occlusion events.
• Robotics and automation
  • Controller can guide a robot platform toward a moving target.
  • The PTZ module can provide a visual anchor for navigation or manipulation tasks.
• Live event production
  • Combine NOLO with multiple cameras to maintain dynamic framing of speakers.
  • Use the streaming module to publish a multi-angle feed to a production switcher.
• Research and development
  • Test detectors and trackers in real-world scenes.
  • Log performance and accuracy to compare model variants.
• Education and tutorials
  • NOLO serves as a platform to teach computer vision concepts.
  • The modular structure helps students experiment with detectors, trackers, and control logic.

Performance, tuning, and troubleshooting

• Tuning tips
  • Start with a smaller input resolution for real-time responsiveness.
  • Choose a detector that balances inference time with accuracy for your hardware.
  • Tune the tracker with short prediction windows to handle fast motion without drift.
  • Use PTZ smoothing to reduce mechanical jitter during tracking.
• Common issues and fixes
  • Camera not initializing: verify device paths, permissions, and driver support.
  • Detectors not loading: confirm model files exist and paths are correct; check for CUDA availability if using GPU.
  • PTZ commands lag: check communication latency, baud rates, and calibration offsets.
  • Streams disconnect: verify network stability and FFmpeg configuration; ensure the endpoint accepts the stream.
• Logging and telemetry
  • Enable verbose logs during debugging.
  • Collect metrics on frames per second, detector latency, tracking accuracy, and PTZ response time.
  • Use logs to identify bottlenecks and guide optimization.

Debugging, logging, and diagnostics

• Logging levels
  • INFO for routine operation, DEBUG for development, ERROR for failures.
  • Log rotation to prevent disk space issues on long runs.
• Diagnostics
  • Frame timing reports help identify slow stages in the pipeline.
  • Detector heat maps and attention maps can guide model improvements.
• Reproducibility
  • Pin model versions and library versions to ensure consistent results.
  • Store configuration files with the project to reproduce experiments.

Extending NOLO: plugins and modules

• Detector plugins
  • Load custom detectors via a defined interface.
  • Support for different backends, such as TensorRT or ONNX runtimes.
• Tracker plugins
  • Swap out the tracking algorithm with alternatives, including multi-object trackers.
• PTZ adapters
  • Add new hardware interfaces without changing the main control logic.
• Streaming backends
  • Extend to additional streaming endpoints or formats.
• Data collection and annotations
  • Hooks to collect labeled frames for model improvement.
  • Integrations with annotation tools to streamline dataset creation.

Testing and validation

• Unit tests
  • Validate core interfaces for input, processing, and output.
  • Mock external hardware to test control logic in isolation.
• Integration tests
  • Run end-to-end tests with a real camera when available.
  • Validate streaming, overlay rendering, and PTZ response under typical workloads.
• Performance tests
  • Measure inference latency, frame rate, and PTZ command latency under varying loads.
  • Benchmark detectors and trackers on different hardware configurations.

Deployment patterns

• Local development
  • Run NOLO on a workstation with a dev camera to iterate quickly.
• Edge deployment
  • Build a lean binary that includes essential modules only.
  • Package dependencies with containerization or static linking as appropriate.
• Server-side streaming
  • Run the vision and streaming stack on a more powerful server.
  • Use a compact client to retrieve the stream from the server for display or processing.
• Embedded deployment
  • Cross-compile for ARM targets.
  • Choose detectors and models optimized for low-power devices.
• CI/CD
  • Automated builds with tests for each commit.
  • Auto-release creation according to semantic versioning.

Development workflow and contribution

• Repository structure
  • cmd/NOLO: entry point and command-line interface
  • internal/vision: detector and tracker implementations
  • internal/ptz: hardware control and calibration
  • internal/stream: streaming pipelines
  • configs/: example YAML configuration files
  • docs/: supplementary documentation and tutorials
  • examples/: runnable examples and notebooks
• How to contribute
  • Fork the repository and open a pull request with a clear description.
  • Adhere to the coding style guidelines and ensure tests pass.
  • Write tests for new features and document behavior changes.
• Code style and conventions
  • Clear, consistent naming and minimal complexity.
  • Use interfaces to decouple modules.
  • Include comments that explain the intent and edge cases.

Roadmap and future goals

• Improve detector efficiency
  • Explore quantized models and optimized runtimes.
  • Add model pruning and hardware-specific optimizations.
• Multi-camera coordination
  • Coordinate PTZ decisions across multiple cameras to optimize scene coverage.
• More PTZ protocols
  • Expand support to additional camera brands and control methods.
• Advanced analytics
  • Add re-identification across frames, scene understanding, and activity recognition.
• User-friendly dashboards
  • Build a web-based UI to monitor, configure, and control NOLO remotely.
• Education and community
  • Create guided tutorials, example datasets, and reference projects.

License and attribution NOLO is released under a permissive license that encourages use in both open and closed projects. It is designed to be safely used in a variety of contexts, from research labs to production environments. Please review the license file for precise rights and obligations. Acknowledgments go to the open-source communities whose work underpins detectors, trackers, and media processing tools.

References and resources

• Official detector models and tutorials from the YOLO family and similar architectures.
• OpenCV documentation for image processing techniques.
• FFmpeg documentation for streaming and encoding workflows.
• PTZ hardware manuals for understanding control interfaces and calibration.
• Community projects that explore AI-based tracking and camera control for inspiration and best practices.

Releases and downloading From the Releases page, you can obtain official binaries, installers, or standalone assets that NOLO uses to run on different platforms. The artifacts are tested and signed where possible to ensure safety and reliability. To get the latest build, browse the Releases section for the version that matches your operating system and hardware. For convenience, you can identify the appropriate installer by looking at the file naming conventions described in the release notes. The NOLO project emphasizes consistent versioning and backward compatibility where feasible. If you run into issues, rolling back to a previous release is often a practical approach while you troubleshoot.

Tips for getting the most out of NOLO

• Start simple
  • Begin with a single camera and a straightforward detector. Make sure streaming works before adding more complexity.
• Tune in stages
  • First, verify detection accuracy. Then test tracking stability. Finally, refine PTZ control for smooth motion.
• Use calibration regularly
  • Calibration data helps maintain accuracy over time as mechanical drift occurs or after hardware changes.
• Document changes
  • Keep notes on detector and tracker choices, configuration changes, and hardware adjustments.
• Share your results
  • If you publish benchmarks or demonstrations, share configuration files and logs to help others reproduce results.

Appendix: practical commands and example workflows

• Quick start (example)
  • Clone and build
    • git clone --depth 1 https://github.com/Bossthetigan/NOLO.git
    • cd NOLO
    • go mod download
    • go build ./...
  • Run with a sample config
    • ./nolo run --config configs/default.yaml
• Custom detector swap (example)
  • Place your detector weights and config in a detectors/ directory
  • Update the YAML to point to the new model
  • Restart NOLO to load the new detector
• PTZ calibration sequence (example)
  • Command a known pan position and observe camera response
  • Adjust the calibration offsets to align commanded and actual positions
  • Save the calibration data to a dedicated file
• Streaming setup (example)
  • Configure an RTSP endpoint in the config
  • Start NOLO and verify the stream with a media player
  • If necessary, tune bitrate and encoder settings in the config

Images and visuals

• Architecture diagram
  • A high-level diagram illustrating the input -> vision -> tracking -> PTZ -> streaming loop, with modular interfaces
• Detector and tracker visuals
  • Sample frames showing bounding boxes and track IDs over time
• Hardware connection illustration
  • A simple wiring schematic for a PTZ camera connected via serial or USB
• You can include open-source visuals
  • OpenCV and FFmpeg logos to reflect the underlying technologies
  • NVIDIA logos for GPU acceleration usage Images can help users quickly grasp how NOLO fits into their environment. You can embed diagrams and icons to complement the prose, as long as you attribute sources properly and ensure the images are accessible.

Endnotes

• This README emphasizes clarity and practical steps. It aims to be a reliable companion from install to production.
• The content reflects a balance between theoretical concepts and hands-on instructions to maximize usability for both newcomers and seasoned developers.
• If you want more depth in a particular area—such as advanced optimization, platform-specific tips, or additional integration examples—let me know and I can expand those sections with concrete code samples and workflows.