Nvidia corporation (20240303836). MULTI-OBJECT TRACKING USING CORRELATION FILTERS IN VIDEO ANALYTICS APPLICATIONS simplified abstract
Contents
MULTI-OBJECT TRACKING USING CORRELATION FILTERS IN VIDEO ANALYTICS APPLICATIONS
Organization Name
Inventor(s)
Joonhwa Shin of Santa Clara CA (US)
Zheng Liu of Los Altos CA (US)
Kaustubh Purandare of San Jose CA (US)
MULTI-OBJECT TRACKING USING CORRELATION FILTERS IN VIDEO ANALYTICS APPLICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240303836 titled 'MULTI-OBJECT TRACKING USING CORRELATION FILTERS IN VIDEO ANALYTICS APPLICATIONS
Simplified Explanation: The patent application describes a method for extracting image areas from a batch of images, scaling them to template sizes, and processing them in parallel on a GPU for localization and filter updates in object tracking.
Key Features and Innovation:
- Batch extraction and scaling of image areas for object tracking.
- Processing image areas in parallel on a GPU for localization and filter updates.
- Learning correlation filters based on focused windowing and occlusion maps.
Potential Applications: This technology can be used in various applications such as surveillance systems, autonomous vehicles, augmented reality, and robotics for object tracking and localization.
Problems Solved: This technology addresses the challenges of efficiently processing image areas for object tracking, improving localization accuracy, and updating filters in real-time.
Benefits:
- Enhanced object tracking performance.
- Faster localization and filter updates.
- Improved accuracy in object tracking systems.
Commercial Applications: Potential commercial applications include security systems, traffic monitoring, industrial automation, and sports analytics for real-time object tracking and analysis.
Prior Art: Prior art related to this technology may include research on GPU-accelerated image processing, object tracking algorithms, and correlation filter-based tracking systems.
Frequently Updated Research: Researchers are constantly exploring new algorithms and techniques to improve object tracking performance, optimize GPU processing for image analysis, and enhance correlation filter learning in tracking systems.
Questions about Object Tracking Technology: 1. How does this technology improve the efficiency of object tracking systems? 2. What are the key advantages of using GPU processing for image analysis in object tracking applications?
Original Abstract Submitted
in various examples, image areas may be extracted from a batch of one or more images and may be scaled, in batch, to one or more template sizes. where the image areas include search regions used for localization of objects, the scaled search regions may be loaded into graphics processing unit (gpu) memory and processed in parallel for localization. similarly, where image areas are used for filter updates, the scaled image areas may be loaded into gpu memory and processed in parallel for filter updates. the image areas may be batched from any number of images and/or from any number of single- and/or multi-object trackers. further aspects of the disclosure provide approaches for associating locations using correlation response values, for learning correlation filters in object tracking based at least on focused windowing, and for learning correlation filters in object tracking based at least on occlusion maps.
