RECOGNITION OF MOVING LIGHT DISPLAYS USING HIDDEN MARKOV-MODELS

A spatio-temporal method of identifying moving light displays (MLDs) is presented. The hidden Markov model (HMM) technique is used as the classification algorithm, making classification decisions based on a spatio-temporal sequence of observed object features. Individual frames of a MLD image sequence are assumed to be segmented and contain very little spatial information. The information content is highly temporal in the sense that image sequences are required for object identification. A single look and alternate multiple frame classifier are used for comparison with the HMM technique. A three-class problem is considered, The single look average classification rate for the moving light display imagery was observed to be near 50%. In contrast, the hidden Markov model average classification rate was above 93%. The alternate nearest neighbor multiple frame technique average classification rate was 20% below the hidden Markov models. A one sided t-test revealed a highly statistically significant difference between the hidden Markov model and multiple frame technique at a 0.01 level of significance.