Improving Processing Speed of Real-Time Stereo Matching using Heterogenous CPU/GPU Model
A. Al-Marakeby1, M Zaki2
1A. Al-Marakeby, Systems and Computers Engineering Dept. , Faculty of Engineering , Al-Azhar University, Cairo, Egypt.
2M. Zaki, Systems and Computers Engineering Dept. , Faculty of Engineering , Al-Azhar University, Cairo, Egypt.
Manuscript received on February 10, 2020. | Revised Manuscript received on February 22, 2020. | Manuscript published on March 10, 2020. | PP: 1983-1987 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2982039520 /2020©BEIESP | DOI: 10.35940/ijitee.E2982.039520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents an improvement of the processing speed of the stereo matching problem. The time required for stereo matching represents a problem for many real time applications such as robot navigation , self-driving vehicles and object tracking. In this work, a real-time stereo matching system is proposed that utilizes the parallelism of Graphics Processing Unit (GPU). An area based stereo matching system is used to generate the disparity map. Four different sequential and parallel computational models are used to analyze the time consumed by the stereo matching. The models are: 1) Sequential CPU, 2) Parallel multi-core CPU, 3) Parallel GPU and 4) Parallel heterogenous CPU/GPU. The dense disparity image is calculated, and the time is highly reduced using the heterogenous CPU/GPU model, while maintaining the same accuracy of other models. A static partitioning of CPU and GPU workload is properly designed based on time analysis. Different cost functions are used to measure the correspondence and to generate the disparity map. A sliding window is used to calculate the cost functions efficiently. A speed of more than 100 frames per second(f/s) is achieved using parallel heterogenous CPU/GPU for 640 x 480 image resolution and a disparity range equals 50.
Keywords: Parallel Computing, stereo Matching, GPU, heterogenous computing, multi-core processors, sliding window.
Scope of the Article: Probabilistic Models and Methods