FPGA Based Acceleration for Object Detection and Tracking Applications
| dc.contributor.guide | Eswaran, P | |
| dc.coverage.spatial | ||
| dc.creator.researcher | Praveenkumar, B | |
| dc.date.accessioned | 2022-12-09T06:51:59Z | |
| dc.date.available | 2022-12-09T06:51:59Z | |
| dc.date.awarded | 2022 | |
| dc.date.completed | 2022 | |
| dc.date.registered | ||
| dc.description.abstract | Advancements in image and video processing are growing over the years for industrial robots, autonomous vehicles, cryptography, surveillance, medical imaging and computer-human interaction applications. One of the major challenges in real-time image and video processing is the execution of complex functions and high computational tasks. To overcome this issue, a hardware acceleration of different filter algorithms for both image and video processing is implemented on Xilinx Zynq®-7000 System on-Chip (SoC) device consists of Dual-core Cortex -A9 processors which provides computing ability to perform with the help of software libraries using Vivado® High-Level Synthesis (HLS) newline | |
| dc.description.note | ||
| dc.format.accompanyingmaterial | DVD | |
| dc.format.dimensions | ||
| dc.format.extent | ||
| dc.identifier.uri | http://hdl.handle.net/10603/423459 | |
| dc.language | English | |
| dc.publisher.institution | Department of Electronics and Communication Engineering | |
| dc.publisher.place | Kattankulathur | |
| dc.publisher.university | SRM Institute of Science and Technology | |
| dc.relation | ||
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Engineering | |
| dc.subject.keyword | Engineering and Technology | |
| dc.subject.keyword | Engineering Electrical and Electronic | |
| dc.title | FPGA Based Acceleration for Object Detection and Tracking Applications | |
| dc.title.alternative | ||
| dc.type.degree | Ph.D. |
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