Bacteria-Filters: Persistent particle filters for background subtraction

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.