- A. Velivelli, Alexander Hauptmann. 2012. Human action recognition using a Markovian conditional exponential model. Abstract: We model the sequence of human actions operating an infusion pump using a Markovian conditional exponential model. We divide each video recorded by a camera into video action units. A video action unit corresponds to the start of a unique human action operation of the infusion pump to the end of that human action operating an infusion pump. We calculate the MOSIFT features of video action units which combines the spatial and temporal dimensions from videos. We vector quantize the MOSIFT features of video action units using K means clustering as video codebook elements. We estimate the conditional exponential model parameters from a training set using maximum entropy constraint and use the video codebook elements as maximum entropy constraint features. We estimate the parameters of the Markovian conditional exponential model from a training set. This Markovian conditional exponential model has 6 states which correspond to the 6 classes of infusion pump operation. To find the optimal state sequence of the Markovian conditional exponential model we use the Viterbi algorithm. This optimal state sequence corresponds to the class label sequence. The infusion pump operation is recorded from 4 video cameras. We calculate the results of classification of 6 classes of infusion pump operation using the conditional exponential model for the 4 video cameras and also we calculate the results of of classification of 6 classes of infusion pump operation using the Markovian conditional exponential model for the 4 video cameras. The classification performance of the Markovian conditional exponential model is better than the classification performance of conditional exponential model.
