Video sequence learning and recognition via dynamic SOM
Information contained in video sequences is crucial for an autonomous robot or a computer to learn and respond to its surrounding environment. In the past, robot vision mainly concentrated on still image processing and small "image cube" processing. Continuous video sequence learning and recognition is rarely addressed in the literature due to its high requirement of dynamic processing. In this paper, we propose a novel neural network structure called dynamic self-organizing map (DSOM) for video sequence processing. The proposed technique has been tested on simulation data sets, and the results validate its learning/recognition ability.