VideoPipe: An Accessible Framework for Video Analysis
Introduction
VideoPipe is a comprehensive framework for video analysis and structuring, crafted in C++ with minimal dependencies. Designed to be easy to use, VideoPipe functions as a modular pipeline where independent nodes can be arranged in numerous configurations. This flexibility makes it ideal for various video analysis applications, such as video structuring, image search, face recognition, and behavior analysis in the domains of traffic and security, including detecting traffic incidents.
Advantages and Features
Comparable to NVIDIA's DeepStream and Huawei's mxVision frameworks, VideoPipe stands out for its ease of use and portability. Here is a comparative overview:
- Open Source: Unlike DeepStream and mxVision, VideoPipe is open source.
- Learning Curve: It features a low learning curve compared to the high learning curve associated with its counterparts.
- Supported Platforms: VideoPipe is platform-independent, allowing usage on any type of hardware.
- Performance: While its performance is noted as medium, its simplicity and reduced dependencies make it highly practical.
- Third-Party Dependencies: VideoPipe has few dependencies, enhancing its portability and ease of setup.
VideoPipe’s plugin-oriented coding facilitates flexible configurations. It employs independent plugins, known as "nodes," to create diverse video analysis solutions. The framework supports multiple inference backends, including OpenCV::DNN (default), TensorRT, PaddleInference, and ONNXRuntime, among others.
Features and Capabilities
- Stream Reading and Video Decoding: Works with various video stream protocols and supports video/image decoding with OpenCV or GStreamer.
- Algorithm Inference: Facilitates multi-level inference using deep learning for tasks like detection, classification, feature extraction, and generation. Traditional image processing algorithms are also supported.
- Object Tracking and Behavior Analysis: Implements object tracking (e.g., IOU, SORT) and behavior analysis, useful for traffic behavior detection (e.g., line-crossing, parking violations).
- Custom Business Logic and Data Integration: Allows integration of business-specific logic and data distribution (JSON, XML) via platforms like Kafka.
- Recording and Display: Provides video recording and frame capture capabilities, with options for video encoding and on-screen data display (OSD).
- Stream Output: Supports streaming in formats such as UDP, RTSP, and RTMP.
Getting Started
VideoPipe can be deployed on various hardware platforms, particularly Ubuntu systems with NVIDIA or Cambrian GPUs. The basic dependencies include C++17, OpenCV, GStreamer, and GCC. Additional backends, like CUDA or TensorRT, may be required if advanced inference capabilities are needed.
Compilation and Usage
To compile VideoPipe, users should clone the repository and perform a CMake build. Post-compilation, executables are placed in designated directories for ease of testing and deployment. VideoPipe is adaptable, allowing users to customize options during the build process, such as enabling CUDA or TensorRT.
How to Use
Users can build sample pipelines by either linking the library or including source code directly. A sample demonstrates video processing for face detection and recognition with dual output options (screen and RTMP stream). The process involves creating and linking nodes like source nodes, inference nodes, OSD nodes, and display or streaming nodes.
Prototype Examples
VideoPipe offers over 40 prototype examples, including face tracking and vehicle tracking. These examples serve as a testament to its versatility in video analysis processes.
Further Reading
For those interested in deeper exploration, VideoPipe provides extensive documentation, including a sample code repository, node overview, and detailed descriptions of its operation and functionality.
Community and Acknowledgements
VideoPipe supports a WeChat discussion group for community interaction and provides appreciation links to platforms that have showcased its potential.
In conclusion, VideoPipe is a robust, open-source option for implementing complex video analysis systems, accessible to a wide audience due to its simplicity and flexibility.