On the computer vision side, we\u2019re able to run our OpenCV model on raspberry pi for plate recognition (detail in Ke\u2019s report). The plate recognition is not 100% accurate (there can be addition\/deletion\/mismatch of letters in the result). To address this issue, we plan to match cars to plate numbers by comparing similarity score.<\/p>\n
On the server side, the management part is mostly finished with blanks left to fill during testing and OpenCV integration. The design of navigation software is finished, and the design of data importation is ongoing (detail in Ruizhe\u2019s report) . Race conditions may be possible for HTTP requests coming in at the same time. Solution to this is still under investigation but should be manageable.<\/p>\n
On the hardware side, multiple sensors were successfully connected with Raspberry Pi to measure distance with adequate accuracy.<\/p>\n
There\u2019s no major modification of the system design, and we\u2019re currently on schedule.<\/p>\n","protected":false},"excerpt":{"rendered":"
On the computer vision side, we\u2019re able to run our OpenCV model on raspberry pi for plate recognition (detail in Ke\u2019s report). The plate recognition is not 100% accurate (there can be addition\/deletion\/mismatch of letters in the result). To address this issue, we plan to match cars to plate numbers by comparing similarity score. On … <\/p>\n