Synchronization of pulse-coupled biological oscillators
SIAM Journal on Applied Mathematics
Towards Multi-Swarm Problem Solving in Networks
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Hand Tracking with Flocks of Features
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
An oscillatory neural model of multiple object tracking
Neural Computation
Brain Areas Specific for Attentional Load in a Motion-Tracking Task
Journal of Cognitive Neuroscience
ACM Computing Surveys (CSUR)
How precise is neuronal synchronization?
Neural Computation
Information transmission in oscillatory neural activity
Biological Cybernetics - Special Issue: Quantitative Neuron Modeling
Combined feature evaluation for adaptive visual object tracking
Computer Vision and Image Understanding
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The world is a dynamic environment hence it is important for the visual system to be able to deploy attention on moving objects and attentively track them. Psychophysical experiments indicate that processes of both attentional enhancement and inhibition are spatially focused on the moving objects; however the mechanisms of these processes are unknown. The studies indicate that the attentional selection of target objects is sustained via a feedforward-feedback loop in the visual cortical hierarchy and only the target objects are represented in attention-related areas. We suggest that feedback from the attention-related areas to early visual areas modulates the activity of neurons; establishes synchronization with respect to a common oscillatory signal for target items via excitatory feedback, and also establishes de-synchronization for distractor items via inhibitory feedback. A two layer computational neural network model with integrate-and-fire neurons is proposed and simulated for simple attentive tracking tasks. Consistent with previous modeling studies, we show that via temporal tagging of neural activity, distractors can be attentively suppressed from propagating to higher levels. However, simulations also suggest attentional enhancement of activity for distractors in the first layer which represents neural substrate dedicated for low level feature processing. Inspired by this enhancement mechanism, we developed a feature based object tracking algorithm with surround processing. Surround processing improved tracking performance by 57% in PETS 2001 dataset, via eliminating target features that are likely to suffer from faulty correspondence assignments.