Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform
1
Robotics and Embedded Systems, Technische Universität München, 80333 München, Germany
2
School of Data and Computer Science, Sun Yat-sen University, Xiaoguwei Island, Panyu District, Guangzhou 510006, China
3
School of Marine Science and Technology, Northwestern Polytechnical University, 127, Youyi West Road, Xi’an 710072, China
4
Integrated Systems, Technische Universität München, 80333 München, Germany
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editors: Xue-Bo Jin, Shuli Sun, Hong Wei and Feng-Bao Yang
Sensors 2017, 17(4), 843; https://doi.org/10.3390/s17040843
Received: 27 February 2017
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Revised: 5 April 2017
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Accepted: 10 April 2017
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Published: 12 April 2017
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications 2017)
While most filtering approaches based on random finite sets have focused on improving performance, in this paper, we argue that computation times are very important in order to enable real-time applications such as pedestrian detection. Towards this goal, this paper investigates the use of OpenCL to accelerate the computation of random finite set-based Bayesian filtering in a heterogeneous system. In detail, we developed an efficient and fully-functional pedestrian-tracking system implementation, which can run under real-time constraints, meanwhile offering decent tracking accuracy. An extensive evaluation analysis was carried out to ensure the fulfillment of sufficient accuracy requirements. This was followed by extensive profiling analysis to spot the potential bottlenecks in terms of execution performance, which were then targeted to come up with an OpenCL accelerated application. Video-throughput improvements from roughly 15 fps to 100 fps (6×) were observed on average while processing typical MOT benchmark videos. Moreover, the worst-case frame processing yielded an 18× advantage from nearly 2 fps to 36 fps, thereby comfortably meeting the real-time constraints. Our implementation is released as open-source code.
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MDPI and ACS Style
Hu, B.; Sharif, U.; Koner, R.; Chen, G.; Huang, K.; Zhang, F.; Stechele, W.; Knoll, A. Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform. Sensors 2017, 17, 843. https://doi.org/10.3390/s17040843
AMA Style
Hu B, Sharif U, Koner R, Chen G, Huang K, Zhang F, Stechele W, Knoll A. Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform. Sensors. 2017; 17(4):843. https://doi.org/10.3390/s17040843
Chicago/Turabian StyleHu, Biao, Uzair Sharif, Rajat Koner, Guang Chen, Kai Huang, Feihu Zhang, Walter Stechele, and Alois Knoll. 2017. "Random Finite Set Based Bayesian Filtering with OpenCL in a Heterogeneous Platform" Sensors 17, no. 4: 843. https://doi.org/10.3390/s17040843
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