Kernelized correlation tracker on smartphones

详细信息    查看全文

• 作者：Danijel Peso^a ; ^b ; ^dpeso@web.de ; Alfred Nischwitz^a ; ^{nischwitz@cs.hm.edu} ; Siegfried Ippisch^a ; ^{siegfried.ippisch@gmail.com} ; Paul Obermeier^b ; ^{paul.obermeier@mbda-systems.de}
• 关键词：Smartphone ; GPGPU ; Correlation tracking ; HOG features ; Color features
• 刊名：Pervasive and Mobile Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：35
• 期：Complete
• 页码：108-124
• 全文大小：5007 K
• 卷排序：35

文摘

This paper shows the implementation of a KC tracker (high-speed kernelized correlation tracker) on an Android smartphone. The image processing part is implemented with the Android-NDK in C/C++. Some parts of the tracking algorithm, which can be parallelized very well, are partitioned and calculated on the GPU with OpenGL ES and OpenCL. Other parts, such as the Discrete Fourier Transform (DFT), are calculated on the CPU (partly with the ARM-NEON features). With these hardware acceleration steps we could reach real-time performance (at least 20–30 FPS) on up-to-date smartphones, such as Samsung Galaxy S4, S5 or Google Nexus 5.Beyond that, we present some new color features and compare their tracking quality to the HOG features using the KC tracker and show that their tracking quality is mostly superior compared to the HOG features.If an object gets lost by the tracker which is the case e.g. if the object is totally hidden or outside the viewing range, there should be a possibility to perform a re-detection. In this paper, we show a basic approach to determine the tracking quality and search for the tracking object in the entire images of the subsequent video-frames.