Powerful foundation model for zero-shot object tracking
Analyze images and videos to identify objects
Track and count objects in videos
Detect objects in images or videos
Process videos to detect and track objects
Detect objects in images or videos
Identify objects in images and videos
Detect objects in a video
Detect and track objects in images or videos
YOLOv11n & DeepSeek 1.5B LLM—Running Locally
Detect objects in images and videos
Detect objects and track body movements in real-time
Detect objects in images, videos, and live webcam streams
Owl Tracking is a powerful foundation model designed for zero-shot object tracking in videos. It enables users to annotate objects in a video based on provided labels, making it an efficient tool for tracking tasks without requiring additional training. This model is particularly useful for applications where accurate and real-time tracking is essential.
• Zero-Shot Tracking: No need for retraining the model for new objects or tasks.
• High Accuracy: Precise object detection and tracking capabilities.
• Real-Time Processing: Performs tracking efficiently, suitable for time-sensitive applications.
• Multi-Object Tracking: Capable of tracking multiple objects simultaneously.
• Customizable Labels: Allows users to define their own labels for objects.
• Scalability: Works effectively across various devices and video resolutions.
What programming language does Owl Tracking support?
Owl Tracking is primarily designed for use with Python, making it compatible with popular machine learning frameworks.
Can Owl Tracking handle fast-moving objects?
Yes, Owl Tracking is optimized to track fast-moving objects with high accuracy in real-time videos.
Is Owl Tracking suitable for non-experts?
Absolutely! Owl Tracking is designed to be user-friendly, requiring minimal setup and configuration to start tracking objects in videos.