W. Yun, Young-Gyu Kim, Yeongmin Lee, Jinyeon Lim, Wonseok Choi, Muhammad Umar Karim Khan, Asim Khan, Said Homidov, Pervaiz Kareem, Hyun Sang Park, C. Kyung
{"title":"Offset aperture based hardware architecture for real-time depth extraction","authors":"W. Yun, Young-Gyu Kim, Yeongmin Lee, Jinyeon Lim, Wonseok Choi, Muhammad Umar Karim Khan, Asim Khan, Said Homidov, Pervaiz Kareem, Hyun Sang Park, C. Kyung","doi":"10.1109/ICIP.2017.8297112","DOIUrl":null,"url":null,"abstract":"Due to the increasing demand for 3D applications, development of novel depth-sensing cameras is being actively pursued. However, most of these cameras still face the challenge of high energy consumption and slow speed in the depth extraction process. This becomes a serious bottleneck in embedded implementations where real-time performance is required, constrained by power and area. This work proposes Offset Aperture (OA) camera, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. Optimal implementations of pre-processing, cost-volume generation and cost-aggregation are presented. The whole depth-extraction pipeline has been implemented on a Field Programmable Gate Array (FPGA). Overall, a mere 2.8% of bad classification was achieved with the proposed system. Also, the proposed system can process 37 VGA frames per second while consuming 0.224 μJ/pixel. High accuracy, speed and low energy consumption of the proposed OA architecture make it suitable for embedded applications.","PeriodicalId":229602,"journal":{"name":"2017 IEEE International Conference on Image Processing (ICIP)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE International Conference on Image Processing (ICIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIP.2017.8297112","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Due to the increasing demand for 3D applications, development of novel depth-sensing cameras is being actively pursued. However, most of these cameras still face the challenge of high energy consumption and slow speed in the depth extraction process. This becomes a serious bottleneck in embedded implementations where real-time performance is required, constrained by power and area. This work proposes Offset Aperture (OA) camera, a new hardware architecture for fast, low-energy, and low-complexity depth extraction. Optimal implementations of pre-processing, cost-volume generation and cost-aggregation are presented. The whole depth-extraction pipeline has been implemented on a Field Programmable Gate Array (FPGA). Overall, a mere 2.8% of bad classification was achieved with the proposed system. Also, the proposed system can process 37 VGA frames per second while consuming 0.224 μJ/pixel. High accuracy, speed and low energy consumption of the proposed OA architecture make it suitable for embedded applications.