Self-Organizing Maps
Artificial Neural Networks for Image Understanding
Artificial Neural Networks for Image Understanding
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Particle Swarms as Video Sequence Inhabitants For Object Tracking in Computer Vision
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 02
The honeybee search algorithm for three-dimensional reconstruction
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
A Real-Time Evolutionary Object Recognition System
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Towards automated learning of object detectors
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
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We present a study on the use of soft computing techniques for object tracking/segmentation in surveillance video clips. A number of artificial creatures, conceptually, "inhabit" our image sequences. They explore the images looking for moving objects and learn their features, to distinguish the tracked objects from other moving objects in the scene. Their behaviour is controlled by neural networks evolved by an evolutionary algorithm while the ability to learn is granted by a Self Organizing Map trained while tracking. Population performance is evaluated on both artificial and real video sequences and some results are discussed.