Anton Dorozhko’s Post

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Senior SWE, ML, and Solutions at SmartCow

NVIDIA's DeepStream is a chained beast of Streaming Video Analytics with Artificial Intelligence. Powers: 1. great plugins with available sources that could infer your AI model in any format at optimal GPU utilization, eat every video, and spit out magical insights in the storage of your choice 2. many sample applications that demonstrate usage of those plugins 3. every version makes steady progress towards ease of use and modification (e.g. ServiceMaker) Chains: 1. difficult to single out some specific function from samples as usually it relies on global context or a lot of utility functions 2. the tools build on top of that (e.g. PipeTuner will also be tightly coupled with how sample apps are organized) Join me on a side quest to free some power-ups of DeepStream. In the first episode, we demonstrate how to extract tracking metadata in KITTI format, convert it to MOT format, and apply tracking evaluation to compute key multi-object tracking (MOT) metrics with TrackEval (one of the standard utilities to compute metrics for many popular MOT benchmarks). https://lnkd.in/erXw2MBn Do you have some proposals for which power-ups could be freed for more modular reuse? Drop a comment or a dm.

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