Bicycle Detection from Top View Perspective in Surveillance System using Convolutional Neural Network
| dc.contributor.author | Ramkumar, Sanal Darshid | |
| dc.contributor.editor | Gesellschaft für Informatik | |
| dc.date.accessioned | 2021-12-15T10:17:11Z | |
| dc.date.available | 2021-12-15T10:17:11Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Bicycle detection and tracking from top view perspective using deep learning is a highly active research area for video surveillance and automatic ticket generation in Advanced Public Transportation System (APTS). People detection using conventional cameras has received massive attention for video surveillance inside public transportation systems but inattentive towards bicycle detection. Experimentation is performed on You Only Look Once (YOLO), Faster Regional-Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD). Due to the sparse availability of dataset for this work, a customized dataset was recorded in the Media Computing lab, Junior Professorship of Media Computing, TU Chemnitz, Germany. The customized dataset was recorded using a wide-angle smart stereo sensor (S2000, Intenta GmbH) mounted in bird’s eye perspective. Furthermore, two additional datasets were recorded using a mobile camera representing indoor and outdoor bicycle parking area. This paper provides best case solution for bicycle detection from a top view perspective. | en |
| dc.identifier.isbn | 978-3-88579-751-7 | |
| dc.identifier.pissn | 1614-3213 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/37784 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | SKILL 2021 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Seminars, Volume S-17 | |
| dc.subject | YOLO | |
| dc.subject | Faster R-CNN | |
| dc.subject | SSD | |
| dc.title | Bicycle Detection from Top View Perspective in Surveillance System using Convolutional Neural Network | en |
| gi.citation.endPage | 100 | |
| gi.citation.startPage | 89 | |
| gi.conference.date | 28. September und 01. Oktober 2021 | |
| gi.conference.location | Berlin | |
| gi.conference.sessiontitle | SKILL 2021 |
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