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\n 中文README | VideoPipe Website | VideoPipe tutorials(视频教程) \n
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\n\n---\n\n## Introduction\n\n`VideoPipe` is a framework for video analysis and structuring, written in C++. It has minimal dependencies and is easy to use. It operates like a pipeline, where each node is independent and can be combined in various ways. `VideoPipe` can be used to build different types of video analysis applications, suitable for scenarios such as video structuring, image search, face recognition, and behavior analysis in traffic/security fields (such as traffic incident detection).\n\n\n\n## Advantages and Features\n\n`VideoPipe` is similar to NVIDIA's DeepStream and Huawei's mxVision frameworks, but it is easier to use and more portable.\n\nHere is a comparison table:\n\n| **Name** | **Open Source** | **Learning Curve** | **Supported Platforms** | **Performance** | **Third-Party Dependencies** |\n|---------------|-----------------|---------------------|--------------------------|-----------------|-------------------------------|\n| DeepStream | No | High | NVIDIA only | High | Many |\n| mxVision | No | High | Huawei only | High | Many |\n| VideoPipe | Yes | Low | Any platform | Medium | Few |\n\n`VideoPipe` uses a plugin-oriented coding style that allows for flexible configuration based on different needs. We can use independent plugins (referred to as `Node` types within the framework) to build various types of video analysis applications. You only need to prepare the model and understand how to parse its output. Inference can be implemented using different backends, such as OpenCV::DNN (default), TensorRT, PaddleInference, ONNXRuntime, or any other backend you prefer.\n\n\n\n## Demonstration\n\nhttps://github.com/sherlockchou86/video_pipe_c/assets/13251045/b1289faa-e2c7-4d38-871e-879ae36f6d50\n\nTo watch in fullscreen, use the button in the bottom right corner of the player，[more video demos](./SAMPLES.md)\n\n## Functions\n\n`VideoPipe` is a framework that simplifies the integration of computer vision algorithm models. It is important to note that it is not a deep learning framework like TensorFlow or TensorRT. The main features of `VideoPipe` are as follows:\n\n- **Stream Reading**: Supports mainstream video stream protocols such as UDP, RTSP, RTMP, file, and application. It also supports image reading.\n- **Video Decoding**: Supports video and image decoding based on OpenCV/GStreamer (with hardware acceleration).\n- **Algorithm Inference**: Supports multi-level inference based on deep learning algorithms, such as object detection, image classification, feature extraction, and image generation. It also supports the integration of traditional image algorithms. **Support mLLM(Multimodal Large Language Model) integration now (update 2025/8/12)**\n- **Object Tracking**: Supports object tracking, such as IOU and SORT tracking algorithms.\n- **Behavior Analysis (BA)**: Supports behavior analysis based on tracking, such as traffic behavior detection like line-crossing, parking, and violations.\n- **Business Logic**: Allows integration of any custom business logic, which can be closely related to specific business requirements.\n- **Data Proxy**: Supports pushing structured data (in JSON, XML, or custom formats) to the cloud, files, or other third-party platforms via methods like Kafka or Soc","github_created_at":"2022-08-26T02:59:12+00:00","created_at":"2026-07-15T11:17:35.146703+00:00","updated_at":"2026-09-20T05:15:33.394278+00:00","categories":[{"slug":"computer-vision","name":"Computer Vision","url":"https://www.graphcanon.com/categories/computer-vision","markdown_url":"https://www.graphcanon.com/categories/computer-vision.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/computer-vision"}],"tags":[{"slug":"ai","name":"ai"},{"slug":"behaviour-analysis","name":"behaviour-analysis"},{"slug":"cv","name":"cv"},{"slug":"deep-learning","name":"deep-learning"},{"slug":"face-recognition","name":"face-recognition"},{"slug":"feature-extraction","name":"feature-extraction"},{"slug":"image-segmentation","name":"image segmentation"},{"slug":"image-classification","name":"image-classification"}],"trust":{"provenance":{"is_fork":false,"github_id":529094231,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-09-20T05:15:31.626Z","maintenance":{"label":"Slowing","score":36,"methodology":"github_public_v1","releases_90d":0,"days_since_push":207,"last_release_at":"2024-04-28T05:46:56Z","stars_delta_30d":63,"open_issues_delta_30d":-4},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-15T11:17:36.362Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-09-20T05:15:32.634Z"},"languages":{"value":["c++"],"source":"github.language","observed_at":"2026-09-20T05:15:32.634Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-09-20T05:15:32.634Z"}},"decision_facts":{"hosting":{"model":"self_hosted","summary":"Users must self-host VideoPipe since no cloud-based service offering was mentioned in the repository data."},"pricing":null,"requirements":null,"constraints":{"hosting_model":"self_hosted"},"when_to_use":["You need a cross-platform solution for building various types of video analysis applications without heavy third-party dependencies.","You require an easy-to-use tool that simplifies the integration of computer vision models, especially if you are working in traffic or security fields."],"when_not_to_use":["If your project requires high performance and can be committed to a specific hardware vendor like NVIDIA or Huawei, DeepStream or mxVision might suit better.","You need advanced features that require deep learning frameworks such as TensorFlow or PyTorch integration beyond VideoPipe's capabilities."],"source":"enrich:decision_facts","observed_at":"2026-07-17T13:48:03.057Z"},"constraint_facets":{"hosting_model":"self_hosted"},"decision_summary":[{"label":"Hosting","value":"self hosted - Users must self-host VideoPipe since no cloud-based service offering was mentioned in the repository data."},{"label":"Adopt for","value":"VideoPipe is a C++-based video analysis framework with minimal dependencies and support for multiple configurations through plugin-oriented design."},{"label":"License detail","value":"VideoPipe is released under the Apache-2.0 license."}]}}