Video Surveillance: Activities in a Cell Area
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Considering todays growing society and developing technologies which are co-influential
between each other, there is a larger scope of security concerns, traffic congestion due to
improper planning and hence a greater need of more intelligent video surveillance.
In this thesis, we have worked on developing such intelligent video surveillance
system which mainly focusses on cell area such as parking spaces. The system operates on
outdoor environment with a stationary camera; the main objective of this system is detecting and tracking of moving objects mainly cars.
Two detection algorithms were developed using optical flow as core strategy. In the
first algorithm the flow vectors were classified based on their magnitude and orientation; the
GOMAG algorithm. The second algorithm used K-means method on the flow vectors to
achieve the classification for moving object detection; the SKMO algorithm.
A comparison analysis was done between the proposed algorithms and well known
detection algorithms of background modeling and Otsu’s segmentation of flow vectors. The
both proposed algorithms performed significantly better than background modeling and
Otsu’s segmentation of flow vectors algorithms. The SKMO algorithm showed better
stability and processed time efficiency than the GOMAG algorithm.
Place, publisher, year, edition, pages
Object Detection, Optical Flow, Motion field, Video Surveillance, Object Tracking, Segmentation.
IdentifiersURN: urn:nbn:se:bth-10729OAI: oai:DiVA.org:bth-10729DiVA: diva2:855990
Subject / course
ET2524 Master's Thesis (120 credits) in Electrical Engineering with emphasis on Signal Processing
Double Diploma program
Khatibi, Siamak, Professor
Johansson, Sven, Professor