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Deep Learning (DL) has revolutionized classic computer vision techniques to enable even more intelligent and autonomous systems. To ease the software development burden for complex embedded visual Deep Learning applications, a multimedia framework, such as GStreamer, is utilized to simplify the task. The Open Source GStreamer audio video streaming framework is a good choice as it separates the complexities of handling streaming video from the inference models processing the individual frames. GstInference is an open-source GStreamer project sponsored by RidgeRun that allows easy integration of deep learning networks into your video streaming application. | Deep Learning (DL) has revolutionized classic computer vision techniques to enable even more intelligent and autonomous systems. To ease the software development burden for complex embedded visual Deep Learning applications, a multimedia framework, such as GStreamer, is utilized to simplify the task. The Open Source GStreamer audio video streaming framework is a good choice as it separates the complexities of handling streaming video from the inference models processing the individual frames. GstInference is an open-source GStreamer project sponsored by RidgeRun that allows easy integration of deep learning networks into your video streaming application. | ||
[[File:Coral example.png|thumb|center|600px|Example of use of GstInference in the Coral. The model used was the TinyYolo V2. Video took from: https://pixabay.com/videos/road-autobahn-motorway-highway-11018/]] | |||
* [https://developer.ridgerun.com/wiki/index.php?title=GstInference GstInference] | [[GstInference|GstInference]] and [[R2Inference| R2Inference]] are supported by the Coral. To install it, follow these guides: | ||
* [[R2Inference/Getting_started/Getting_the_code|Getting R2Inference code]] | |||
* [[R2Inference/Getting_started/Building_the_library|Building R2Inference]]: The Google Coral is optimized for Tensorflow Lite. To install TFLite for ARM64 follow this [https://developer.ridgerun.com/wiki/index.php?title=R2Inference/Supported_backends/TensorFlow-Lite#Cross-compile_for_ARM64 guide]. | |||
* [[GstInference/Getting_started/Getting_the_code|Getting GstInference code]] | |||
* [[GstInference/Getting_started/Building_the_plugin|Building GstInference]] | |||
After the installation is completed, you can generate GStreamer pipelines for the Coral using different GstInference elements in this [[GstInference/Example_pipelines_with_hierarchical_metadata| tool]]. | |||
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