Uncategorized | SAVANT

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.

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.

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.

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.

RocksQ – a New Blazingly Fast Persistent Queue For Python

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.

Savant Blog Switches From Medium To WordPress

We decided to move the Savant blog from the Medium platform to a self-hosted blog platform. The reason for that is that the articles on Medium are behind a paywall which reduces the traffic. Old articles are available on Medium.

Savant 0.2.5 is Out: What is New

We are proud to present you with a new Savant version — 0.2.5. We worked on the release for more than 2.5 months. It contains significant changes, new features, and bug fixes in several fields, but primarily, we improved developer experience and deployment features.

OpenTelemetry in Savant: Instrumenting Deep Learning Computer Vision Pipelines

OpenTelemetry is the industry standard for code instrumenting widely used in modern complex applications. It shines bright in distributed, multithreaded, and asynchronous systems. With OpenTelemetry, developers can trace, log, and collect metrics.

Facial Identification With Savant, YOLOV5-Face, AdaFace and HNSWLIB

Facial re-identification is a commodity task in the CV field: there is no rocket science in doing that, at least academically. However, the commercial efficiency of such a solution is still a concern for customers. The article presents a high-performance pipeline developed with the Savant framework, which can be used in doorbell security or video content annotation systems.

Real-Time Instance Segmentation With YOLOV8M-seg And Savant Framework

Instance segmentation is an important task in the computer vision field. High-quality instance segmentation couldn’t run in real-time on more than new hardware for a long time. However, recent advances in the CV field have made it possible to run instance segmentation in real-time. The YOLOV8 family, invented and published by Ultralitics, broke through the next frontier of computer vision, enabling object segmenting efficiently. It opens doors for a broad range of applications and increases the quality of CV, which is more important.