wang7393 / gst-video-analytics

This repository contains a collection of GStreamer* elements to enable CNN model based video analytics capabilities (such as object detection, classification, recognition) in GStreamer* framework.

GStreamer Video Analytics (GVA) Plugin

Overview

This repository contains GStreamer elements that enable CNN model-based video analytics capabilities in the GStreamer framework. These elements include such things as object detection, classification, and recognition. Example above shows concise GStreamer pipeline that runs detection & emotion classification, using specific models on a video file:

gst-launch-1.0 filesrc location=cut.mp4 ! decodebin ! videoconvert ! gvadetect model=face-detection-adas-0001.xml ! gvaclassify model=emotions-recognition-retail-0003.xml model-proc=emotions-recognition-retail-0003.json ! gvawatermark ! xvimagesink sync=false

The solution leverages:

• Open-source GStreamer framework for pipeline management
• GStreamer plugins for input and output, such as media files and real-time streaming from a camera or network
• Video decode and encode plugins, including either CPU-optimized plugins or GPU-accelerated plugins, based on VAAPI

In addition, the solution installs the following Deep Learning-specific elements, also available in this repository:

• Inference plugins leveraging OpenVINO^™ Toolkit for high-performance inference using CNN models
• Visualization of computer vision results (such as bounding boxes and labels of detected objects) on top of video stream

License

The GStreamer Video Analytics Plugin is licensed under the MIT license.

Prerequisites

Hardware

• OpenVINO^™ Toolkit has information about the hardware requirements for inference elements
• On platforms with Intel Gen graphics, see the gstreamer-vaapi for hardware accelerated video decode and encode requirements

Software

• OpenVINO^™ Toolkit 2019 R2 (Inference Engine 2.0.0) or above
• Linux* system with kernel 4.15 or above
• GStreamer framework 1.14 or above

Getting Started

• Start here: Getting Started Guide
• API reference

Samples

See the command-line examples and C++ example

Reporting Bugs and Feature Requests

Report bugs and requests on the issues page

Usage and integration into application

Pipelining and data flow

Details about pipeline construction and the data flow between pipeline elements

Metadata

Details about metadata generated by inference plugins and attached to video frames

Model preparation

Details about how to prepare Tensorflow*, Caffe*, and other models for the inference plugins

Plugins parameters

Elements list and properties list for each element

How to contribute

Pull requests aren't monitored, so if you have bug fix or an idea to improve this project, post a description on the issues page.

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