Details

Autor(en) / Beteiligte

• Dugad, Shashikant
• Puliyadi, Vijayalakshmi
• Palod, Heet
• Johnson, Nidhi
• Rajput, Simran
• Johnny, Swapna

Titel

Ship intrusion detection security system using image processing & SVM

Ist Teil von

• 2017 International Conference on Nascent Technologies in Engineering (ICNTE), 2017, p.1-7

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEEE Electronic Library Online

Beschreibungen/Notizen

• Surveillance is a paramount problem for harbor protection, border control and security of various commercial facilities. It is particularly challenging to protect the vast near-coast sea surface and busy harbor areas from intrusions of unauthorized marine vessels, such as trespassing boats and ships. In our project, we present a state-of-the-art solution for ship intrusion detection using image processing and Support Vector Machine (SVM). The main aim is to detect the ships, which cross over the border and secured industrial spaces. Using the interworking mechanisms of these two techniques, we can detect the intruding ship from the constantly changing sea environment. SVM can be used as a machine learning to train the system by exposing it to different seashore environments. Hence, it can be used as a real time security system at seashore areas.