Real-Time Resource Allocation for Tracking Systems
This addresses the challenge of real-time resource allocation for tracking systems, particularly in high-resolution images, offering a practical improvement for computer vision applications.
The paper tackles the problem of real-time tracking in computer vision by reducing computational cost through selective detection, achieving real-time performance with only 10% of pixel boxes processed while retaining 80% of tracking performance.
Automated tracking is key to many computer vision applications. However, many tracking systems struggle to perform in real-time due to the high computational cost of detecting people, especially in ultra high resolution images. We propose a new algorithm called \emph{PartiMax} that greatly reduces this cost by applying the person detector only to the relevant parts of the image. PartiMax exploits information in the particle filter to select $k$ of the $n$ candidate \emph{pixel boxes} in the image. We prove that PartiMax is guaranteed to make a near-optimal selection with error bounds that are independent of the problem size. Furthermore, empirical results on a real-life dataset show that our system runs in real-time by processing only 10\% of the pixel boxes in the image while still retaining 80\% of the original tracking performance achieved when processing all pixel boxes.