Don't miss the upcoming webinar: Building Real-Time Data Pipelines with a 3rd Generation Stream Processing Engine - sign up now!
Get it on Github

Real-time Image Recognition

Machine LearningLive FeedStreamingWindowingAggregationIMDG StorageCustom SourceCustom SinkPipeline API

This demo uses the webcam video stream of a laptop computer as a source and recognizes the objects using machine learning. The image classification is performed using a convolutional neural network pre-trained using a CIFAR-10 dataset.

The model is read from the filesystem and loaded into Jet processors which enable the recognition. The video is streamed from a local camera and decomposed to individual frames. Every frame is passed to an image recognition algorithm and classified. The image recognition results are aggregated in 1 second windows and the frame with the maximum score is shown in a GUI application.

DAG Visualization

Output

Hazelcast Jet

Main Menu