Recent Posts

Savant, a new open-source product that simplifies the use of Nvidia DeepStream for ML engineers. It is a framework that handles the heavy lifting, allowing you to focus on building optimized inference pipelines with declarative syntax and Python functions.

With Savant, you can easily handle multiple streams simultaneously, deliver reliable, production-ready inference pipelines quickly and achieve top-notch performance with TensorRT. Visit the website to know more.

0.2.1 Release Information

In 0.2.1, we fixed several bugs, created 2 more demos, and implemented multiple features to make it more developer friendly. With 0.2.1, you can easily develop pipelines with PyCharm Professional + Docker Runtime (docs in progress).

• New demo: Efficient City Traffic Metering With PeopleNet/YOLOv8, Savant, And Grafana At Scale;
• New adapter: Video Loop Adapter;
• Developer documentation: link;
• Release Information;
• Discord.

With Savant, you can easily handle multiple streams simultaneously, deliver reliable, production-ready inference pipelines quickly and achieve top-notch performance with TensorRT. Visit the website to know more.

Up until this point, we published three articles on Savant demonstrating how to use it to solve detection, tracking, and classification tasks efficiently:

• Meet Savant: a New High-Performance Python Video Analytics Framework For Nvidia Hardware
• Building a 500+ FPS Accelerated Video Background Removal Pipeline with Savant and OpenCV CUDA MOG2
• Building a High-Performance Car Classifier Pipeline With Savant

As an ML engineer, you know that training is only half the battle. But what if you could streamline the process of providing solutions for inference without sacrificing performance or getting bogged down in complex internals?

We are introducing Savant, a new open-source product that simplifies the use of Nvidia DeepStream for ML engineers. It is a framework that handles the heavy lifting, allowing you to focus on building optimized inference pipelines with declarative syntax and Python functions.

With Savant, you can easily handle multiple streams simultaneously, deliver reliable, production-ready inference pipelines quickly and achieve top-notch performance with TensorRT. Visit the website to know more.

At the onset of developing computer vision systems, developers often rely on readily available open-source implementations for trackers, such as SORT and DeepSORT, which can easily be found on the internet.

The main reason for their popularity is that these solutions are presented by the algorithms’ authors themselves and have gathered huge attention from those days. As a result, developers may think that they are the best options available, which is wrong, because despite those implementations are correct, they are not optimized for speed.

When tracking objects in real-time, it is crucial to have a fast and accurate system in place. Slow trackers can cause delays, missed detections, and even wrong classifications, leading to severe consequences in critical applications. For example, in autonomous driving, a slow tracker can cause the car to miss important objects like pedestrians or other vehicles, leading to potentially deadly accidents.

Another issue with slow trackers is that they require more computational power and resources, making them less suitable for low-powered devices or real-time applications. This issue can significantly limit the potential use cases for computer vision systems and make them less applicable for the edge.

Our new article describes the problems which a slow object tracker may cause and how they affect on AI pipeline performance, economy and bottlenecks.

Oriented or rotated object tracking can be necessary in situations where objects of interest can be present in various orientations in the image or video footage. This can occur, for example, when tracking vehicles, where the orientation of the car may vary as it moves around in the scene.

Similarly, when tracking people, their orientation may change as they move and turn. In such cases, using an oriented bounding box to track the object can improve the accuracy of the tracking results. Additionally, oriented object tracking can also be useful in applications such as surveillance, robotics, and autonomous vehicles.