Study on the Moving Target Tracking Based on Vision DSP

Sensors
Xuan GongYukun Wu

Abstract

The embedded visual tracking system has higher requirements for real-time performance and system resources, and this is a challenge for visual tracking systems with available hardware resources. The major focus of this study is evaluating the results of hardware optimization methods. These optimization techniques provide efficient utilization based on limited hardware resources. This paper also uses a pragmatic approach to investigate the real-time performance effect by implementing and optimizing a kernel correlation filter (KCF) tracking algorithm based on a vision digital signal processor (vision DSP). We examine and analyze the impact factors of the tracking system, which include DP (data parallelism), IP (instruction parallelism), and the characteristics of parallel processing of the DSP core and iDMA (integrated direct memory access). Moreover, we utilize a time-sharing strategy to increase the system runtime speed. These research results are also applicable to other machine vision algorithms. In addition, we introduced a scale filter to overcome the disadvantages of KCF for scale transformation. The experimental results demonstrate that the use of system resources and real-time tracking speed also satisfies the expected ...Continue Reading

References

Jul 17, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·Pedro F FelzenszwalbDeva Ramanan
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·João F HenriquesJorge Batista
Sep 23, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Martin DanelljanMichael Felsberg
Jun 10, 2020·Sensors·Giuseppe ContiDavid Jiménez

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Methods Mentioned

BETA
chip
feature extraction

Software Mentioned

Xplorer
IVP
Cadence
iDMA
Visual DSP
DSP
Vision DSP

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