Tracking fast changing non-stationary distributions with a topologically adaptive neural network: application to video tracking

In this paper, an original method named GNG-T, extended from GNG-U algorithm by [1] is presented. The method performs continuously vector quantization over a distribution that changes over time. It deals with both sudden changes and continuous ones, and is thus suited for video tracking framework, where continuous tracking is required as well as fast adaptation to incoming and outgoing people. The central mechanism relies on the management of quantization resolution, that cope with stopping condition problems of usual Growing Neural Gas inspired methods. Application to video tracking is briefly presented.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Georges Adrian Drumea, Hervé Frezza-Buet
• Keywords: ESANN 2007, Neural Networks, Vector Quantization, Video Tracking, Video Tracking Framework