TY - GEN
T1 - Multi objective optimization based fast motion detector
AU - Su, Jia
AU - Wei, Xin
AU - Jin, Xiaocong
AU - Ikenaga, Takeshi
N1 - Funding Information:
This research was supported by “Ambient SoC Global COE Programof Waseda University” of the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.
PY - 2011
Y1 - 2011
N2 - A large number of surveillance applications require fast action, and since many surveillance applications, motive objects contain most critical information. Fast detection algorithm system becomes a necessity. A problem in computer vision is the determination of weights for multiple objective function optimizations. In this paper we propose techniques for automatically determining the weights, and discuss their properties. The Min-Max Principle, which avoids the problems of extremely low or high weights, is introduced. Expressions are derived relating the optimal weights, objective function values, and total cost. Simulation results show, compared to the conventional work, it can achieve around 40% time saving and higher detection accuracy for both outdoor and indoor surveillance videos.
AB - A large number of surveillance applications require fast action, and since many surveillance applications, motive objects contain most critical information. Fast detection algorithm system becomes a necessity. A problem in computer vision is the determination of weights for multiple objective function optimizations. In this paper we propose techniques for automatically determining the weights, and discuss their properties. The Min-Max Principle, which avoids the problems of extremely low or high weights, is introduced. Expressions are derived relating the optimal weights, objective function values, and total cost. Simulation results show, compared to the conventional work, it can achieve around 40% time saving and higher detection accuracy for both outdoor and indoor surveillance videos.
KW - Motion detection
KW - Multi-objective optimization
KW - Video surveillance
UR - http://www.scopus.com/inward/record.url?scp=78751671040&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78751671040&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-17832-0_46
DO - 10.1007/978-3-642-17832-0_46
M3 - Conference contribution
AN - SCOPUS:78751671040
SN - 3642178316
SN - 9783642178313
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 492
EP - 502
BT - Advances in Multimedia Modeling - 17th International Multimedia Modeling Conference, MMM 2011, Proceedings
T2 - 17th Multimedia Modeling Conference, MMM 2011
Y2 - 5 January 2011 through 7 January 2011
ER -