How To Do Car License Plate Recognition With YOLOv8 And LPD/LPR Models From Nvidia NGC

In the realm of computer vision, the ability to recognize and read license plates from vehicles is a significant advancement. This technology has numerous applications, from traffic management to security and surveillance. Three prominent models that have made this possible are YOLOv8 and the License Plate Detection/Recognition (LPD/LPR) models from Nvidia NGC.

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Ten Reasons To Consider Savant For Your Computer Vision Project

This article answers the question of why you may find it beneficial to use Savant instead of DeepStream, OpenVino, PyTorch, or OpenCV in your next computer vision project. It is not an easy question, because computer vision is a tough field with many caveats and difficulties. You start with finding a way to make certain things doable from the quality point of view, but later you also need to serve the solution commercially efficiently, processing data in real-time rather than pathetic 2 FPS on hardware worth like a Boeing wing.

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Crafting Scalable Computer Vision: Video Analytics with Kafka and KeyDB

There are two types of computer vision applications around us: the first ones deliver rapidly fast, instant image or video processing to signal about critical situations like car accidents, people in danger zones, or serious outages in factories; others handle data on demand or in a delayed manner to deliver the knowledge at scale. Those two kinds have in common that they process video data, but their differences are too significant to ignore.

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Approaches On How To Surpass Real-Time Video Analytics Challenges

Real-time video analytics is a hot topic today. Needless to say, visual information is essential for us: it is estimated that about 80–90% of the information we receive comes through our sense of sight. Moreover, the human visual system is remarkably efficient at processing a vast amount of information; our visual processing centers are highly adept at interpreting and organizing visual information, allowing us to make sense of the world around us quickly and efficiently.

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How To Run Real-Time Vehicle Traffic Analysis With YOLOV8, Graphite And Grafana

The pipeline calculates vehicles passing from a source edge to a destination edge and sends statistics to Grafana for visualization. The user configures a per-cam traffic metering zone with a polygonal area and labels its lines with assigned tags for metering traffic passing through them.

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Dynamic AI Pipeline Parameters Reconfiguration With Etcd In Savant Framework

Computer vision and artificial intelligence inference pipelines often begin as straightforward, statically defined conveyers, but soon begin to require additional heuristics to optimize their performance and adapt to real-life conditions. Let us consider several situations which may require pipelines to be reconfigured dynamically. We will not cover the cases that require parameters applied for good: as they can be passed to the pipeline through environment variables or configuration files.

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RocksQ – a New Blazingly Fast Persistent Queue For Python

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When developing Savant, we create auxiliary technologies that can be used in other projects. We would like to share with you a new project developed by the Savant team – RocksQ. We needed a high-performance persistent queue to buffer video frames and metadata in situations when the receiving party is out of order. Previously, we used persist-queue, a Pythonic persistent queue on top of Sqlite. However, it is obviously an overhead to use a full-scale embedded SQL database just for queueing.

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