| dc.creator |
Harteno, Rudy |
|
| dc.date |
2020 |
|
| dc.date.accessioned |
2020-06-17T01:52:19Z |
|
| dc.date.available |
2020-06-17T01:52:19Z |
|
| dc.identifier |
http://eprints.ulm.ac.id/9527/1/document-halaman-1.pdf |
|
| dc.identifier |
Harteno, Rudy (2020) OBJECT COUNTING PADA DATA VIDEO. OBJECT COUNTING PADA DATA VIDEO, 7 (1). ISSN ISSN: 2406-7857 |
|
| dc.identifier.uri |
https://repo-dosen.ulm.ac.id//handle/123456789/16057 |
|
| dc.description |
One object counting implementation is counting the number of road users from video data sources obtained from CCTV streaming. Video processing on CCTV is usually done on the server side by sending video data. If the need is only to determine the density of traffic, then the method is considered too expensive to be implemented because of the cost of internet connection and bandwidth that must be spent. The solution is to use a small computing device that can process the video first, and the calculation results are sent to the server regularly. In this study, a comparison between the Tensorflow Object Counting learning algorithm and the MOG2 Background Subtractor image processing algorithm with the aim to determine the accuracy of the calculation. The result is known that better accuracy is given by the MOG2 Background Subtractor technique and also the process is carried out using only a small percentage of the amount of memory and processor compared to the Tensorflow Object Counting technique. MOG2 Background Substractor technique is expected to be used on devices that have small data sources
Keywords : Object Counting, Tensorflow, MOG2 Background Substractor. |
|
| dc.format |
text |
|
| dc.publisher |
Kumpulan jurnaL Ilmu Komputer (KLIK) |
|
| dc.relation |
http://eprints.ulm.ac.id/9527/ |
|
| dc.subject |
Q Science (General) |
|
| dc.title |
OBJECT COUNTING PADA DATA VIDEO |
|
| dc.type |
Article |
|
| dc.type |
PeerReviewed |
|