Performance-Intensive Computing Helps Lodestar Computer Vision ‘Index’ Video Data

Lodestar is a complete management suite for developing artificial intelligence-based computer vision models from video data. It can handle the navigation and curation of a native video stream without any preparation. Lodestar annotates and labels video, and using artificial intelligence, creates searchable, structured data.

  • September 21, 2022 | Author: David Strom
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Lodestar doesn’t call it indexing, but the company has a product that annotates video, and using artificial intelligence (AI), creates searchable, structured data. Lodestar offers a complete management suite for developing AI-based computer vision models from video data. The company’s technology includes continuous training of its AI models along with real-time active learning and labeling.

 

The challenge for computer vision efforts before Lodestar's technology came into the picture was the sheer amount of data contained in any video stream: an hour of video contains trillions of pixels. The result was a very heavy computational load to manipulate and analyze. That meant video had to be pre-processed before anyone could analyze the stream. But thanks to performance-intensive computing, there are new ways to host more capable and responsive tools.

 

That's where Lodestar comes into play, handling the navigation and curation of a native video stream without any preparation, using the video as a single source of truth. Metadata is extracted on the fly so that each video frame can be accessed by an analyst. This is a highly CPU-intensive process, and Lodestar uses Supermicro A+ servers running Jupyter’s data science applications across a variety of containers. These servers have optimized hardware that combines AMD CPU and GPU chipsets with the appropriate amount of memory to make these applications function quickly.

 

By harnessing this power, data scientists can now collaborate in real time to validate the dataset, run experiments, train models and guide annotation. With Lodestar, data scientists and domain experts can develop a production AI in weeks instead of months.

 

That’s what a leading European optical and hearing aid retailer did to help automate its in-store inventory management processes and keep track of its eyewear collection. Before the advent of Lodestar, each store’s staff spent 10 hours a month manually counting inventory. That doesn’t sound like much until you multiply the effort by 300 stores. With Lodestar, store inventory is completed in minutes. Given that the stores frequently update their product offerings, this has brought significant savings in labor, and more accurate inventory numbers have provided a better customer experience.