Object Detection & Tracking
Object Detection & Tracking on the play Video Intelligence platform leverages the robust Ultralytics model architecture, providing a powerful foundation for detecting and tracking objects across video frames. This chapter explores how the system utilizes advanced models and Large Language Models (LLMs) to deliver precise and efficient object detection and tracking capabilities.
Key Features
- Ultralytics Model Architecture: Built on the highly adaptable Ultralytics framework, the platform supports a variety of models tailored to specific tasks, including object detection, instance segmentation, image classification, pose estimation, and multi-object tracking.
- Zero-Shot Detection: Utilizing zero-shot detection capabilities, the system can identify and track objects based on user queries without the need for prior training on specific datasets.
- LLM as Translation Layer: Large Language Models serve as a translation layer, converting natural language prompts into actionable queries for the detection models. This allows users to describe objects in plain language and receive accurate detection results.
How It Works
- User Queries: Users can input natural language queries to describe the objects they want to detect or track in the video. For example, "Track the person wearing a red shirt" or "Detect all cars in the parking lot."
- LLM Translation: The system uses LLMs to translate these natural language prompts into specific queries that the detection models can understand.
- Model Execution: The Ultralytics-based detection models process these queries, leveraging their extensive training on diverse datasets to perform the required tasks accurately.
- Results Delivery: The detected objects and tracking information are presented to the user, allowing for further analysis or action.
Supported Models
Play Video Intelligence supports a range of models within the Ultralytics framework, each designed for specific tasks:
- Object Detection: Identifies and locates objects within video frames.
- Instance Segmentation: Differentiates between multiple instances of the same object type.
- Image Classification: Categorizes objects within images.
- Pose Estimation: Detects and tracks human poses.
- Multi-Object Tracking: Continuously tracks multiple objects across video frames.
For more detailed information, see the list of supported models