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Distributed Visual Processing Based On interest Point Clustering
KTH, School of Electrical Engineering (EES), Communication Networks.
2015 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Distribuerad visuell bearbetning baserad på intresse punkt kluster (Swedish)
Abstract [en]

In this master thesis project, we study the problem in Visual Sensor Networks

in which only limited bandwidth is provided. The task is to search for ways to

decrease the transmitting data on the camera side, and distribute the data to dif-

ferent nodes.

To do so, we extract the interest points on the camera side by using BRISK in-

terest point detector, and we distribute the detected interest points into di erent

number of processing node by implementing proposed clustering methods, namely,

Number Based Clustering, K-Means Clustering and DBSCAN Clustering.

Our results show it is useful to extract interest points on the camera side, which

can reduce almost three quarters of data in the network. A step further, by imple-

menting the clustering algorithms, we obtained the gain in overhead ratio, interest

point imbalance and pixel processing load imbalance, respectively. Specically,

the results show that none of the proposed clustering methods is better than oth-

ers. Number Based Clustering can balance the processing load between di erent

processing nodes perfectly, but performs bad in saving the bandwidth resources.

K-Means Clustering performs middle in the evaluation while DBSCAN is great in

saving the bandwidth resources but leads to a bad processing balance performance

among the processing nodes.

Place, publisher, year, edition, pages
2015. , 53 p.
EES Examensarbete / Master Thesis
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-168013OAI: diva2:813858
Educational program
Master of Science - Network Services and Systems
Available from: 2015-05-25 Created: 2015-05-25 Last updated: 2015-05-25Bibliographically approved

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