Scalable implementation of particle filter-based visual object tracking on network-on-chip (NoC)

• Published in: Journal of real-time image processing Vol. 17; no. 5; pp. 1117 - 1134
• Main Authors: Engineer, Pinalkumar, Velmurugan, Rajbabu, Patkar, Sachin
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2020; Springer Nature B.V
• Subjects: Algorithms; Approximation; Computer Graphics; Computer Science; Field programmable gate arrays; Histograms; Image Processing and Computer Vision; Multimedia Information Systems; Multiple target tracking; Optical tracking; Original Research Paper; Pattern Recognition; Power consumption; Random noise; Signal,Image and Speech Processing; System on chip; Tracking; Real-time image processing; Smart camera; Scalability; FPGA; Network-on-chip (NoC); Low power; Object tracking; Particle filter
• ISSN: 1861-8200; 1861-8219
• DOI: 10.1007/s11554-018-0841-5

Particle filter algorithms have been successfully used in various visual object tracking applications. They handle non-linear model and non-Gaussian noise, but are computationally demanding. In this paper, we propose a scalable implementation of particle filter algorithm for visual object tracking, using scalable interconnect such as network-on-chip on an FPGA platform. Here, several processing elements execute parallelly to handle large number of particles. We propose two designs and implementations, with one optimized for speed and other optimized for area. These implementations can easily support different image sizes, object sizes, and number of particles, without modifying the complete architecture. Multi-target tracking is also demonstrated for four objects. We validated the particle filter-based visual tracking with video feed from a Petalinux-based system. With image size of 320 × 240 , frame rates of 348 fps and 310 fps were achieved for single-object tracking of size 17 × 17 and 33 × 33 pixels, respectively, with a reasonable low-power consumption of 1.7 mW/fps on Zynq XC7Z020 (Zedboard) with an operating frequency of 69 MHz. This makes our implementation a good candidate for low-power, visual object tracking using FPGA, especially in low-power, smart camera applications.