Tracking and data association
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Neural Networks for Optimization and Signal Processing
Neural Networks for Optimization and Signal Processing
Development of the Multi-target Tracking Scheme Using Particle Filter
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
Application of the Particle Filter for Simple Gesture Recognition
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
A study on the gesture recognition based on the particle filter
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Automation of virtual interview system using the gesture recognition of particle filter
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Development of the hopfield neural scheme for data association in multi-target tracking
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Implementation of interactive interview system using hand gesture recognition
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
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This paper introduces an adaptive algorithm determining the measurement-track association problem in multi-target tracking. We model the target and measurement relationships and then define a MAP estimate for the optimal association. Based on this model, we introduce an energy function defined over the measurement space, that incorporates the natural constraints for target tracking. To find the minimizer of the energy function, we derived a new adaptive algorithm by introducing the Lagrange multipliers and local dual theory. Through the experiments, we show that this algorithm is stable and works well in general environments.