Increamental Focus of Attention (IFA)
Mistracking in visual tracking has been recognized as being unavoidable. Instead of improving the tracking algorithm itself, this idea use the attention mechanism to improve the robustness of the tracking system. Lossing the tracking target is tolerant in this kind of system. However, the IFA offers the automatic initialization and reinitialization when the environmental conditions momentarily deteriorate or target motion is temporally unexpected. The Computational Interaction and Robotics Lab of Johns Hopkins University has proposed a framework of IFA tracking (http://www.cs.jhu.edu/CIPS/ifa/). The system includes two modules: selector (search for possible configuration of target) and tracker (tracking the locations of target). When the target losts, the attention mechanism is invoked to reinitilize the target. Multiple tracking algorithms with different precision are applied. If the current tracking algorithm fails, a less precise algorithm take over. The whole system is organized as different layer where processing occurs only in one layer.
However, without reading the paper, I don't know what kind of attention mechanism is designed in IFA. (Any new increamental attention model can be designed?) Here, the most important factor different from the attention model in images is the increamental, how to make attention increamental and more efficient. IFA is somehow related to top-down attention mechanism because at the beginning, the system tracks the target and have a model of the target, the attention in this situation in IFA is defined by this kind of priori knowledge instead of bottom-up data-driven data. How to design new IFA for tracking?
