TY - GEN
T1 - Bacteria-filters
T2 - 2010 17th IEEE International Conference on Image Processing, ICIP 2010
AU - Movshovitz-Attias, Yair
AU - Peleg, Shmuel
PY - 2010
Y1 - 2010
N2 - Moving objects are usually detected by measuring the appearance change from a background model. The background model should adapt to slow changes such as illumination, but detect faster changes caused by moving objects. Particle filters do an excellent task in modeling non parametric distributions as needed for a background model, but may adapt too quickly to the foreground objects. A persistent particle filter is proposed, following bacterial persistence. Bacterial persistence is linked to the random switch of bacteria between two states: A normal growing cell and a dormant but persistent cell. The dormant cells can survive stress such as antibiotics. When a dormant cell switches to a normal status after the stress is over, bacterial growth continues. Similar to bacteria, particles will switch between dormant and active states, where dormant particles will not adapt to the changing environment. A further modification of particle filters allows discontinuous jumps into new parameters enabling foreground objects to join the background when they stop moving. This can also quickly build multi-modal distributions.
AB - Moving objects are usually detected by measuring the appearance change from a background model. The background model should adapt to slow changes such as illumination, but detect faster changes caused by moving objects. Particle filters do an excellent task in modeling non parametric distributions as needed for a background model, but may adapt too quickly to the foreground objects. A persistent particle filter is proposed, following bacterial persistence. Bacterial persistence is linked to the random switch of bacteria between two states: A normal growing cell and a dormant but persistent cell. The dormant cells can survive stress such as antibiotics. When a dormant cell switches to a normal status after the stress is over, bacterial growth continues. Similar to bacteria, particles will switch between dormant and active states, where dormant particles will not adapt to the changing environment. A further modification of particle filters allows discontinuous jumps into new parameters enabling foreground objects to join the background when they stop moving. This can also quickly build multi-modal distributions.
KW - Background model
KW - Object detection and tracking
KW - Particle filter
KW - Tracking filters
UR - http://www.scopus.com/inward/record.url?scp=78651246386&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2010.5653118
DO - 10.1109/ICIP.2010.5653118
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AN - SCOPUS:78651246386
SN - 9781424479948
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 677
EP - 680
BT - 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Y2 - 26 September 2010 through 29 September 2010
ER -