Real-Time Resource Allocation for Tracking Systems

• URL: http://arxiv.org/abs/2010.03024v1
• Date: Mon, 21 Sep 2020 08:29:05 GMT
• Title: Real-Time Resource Allocation for Tracking Systems
• Authors: Yash Satsangi, Shimon Whiteson, Frans A. Oliehoek, Henri Bouma
• Abstract summary: We propose a new algorithm called emphPartiMax 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 emphpixel boxes in the image. We 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.
• Score: 54.802447204921634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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