Detection and recognition of moving objects using the temporal difference method and the hidden Markov model
Wanhyun Cho, Sunworl Kim, Gukdong Ahn · 2011
This paper proposes a new detection and recognition method for moving objects that uses the temporal difference method (TDM) and the hidden Markov model (HMM). First, we apply the concept of entropy to convert the pixel value in the image domain into the amount of energy change in the entropy domain. Second, we use the temporal difference method to quickly detect the region of moving objects in complex images to address the variation in changing environments. Third, we use the discrete wavelet transformation technique to extract proper feature vectors from the detected mask image. Fourth, we use the hidden Markov model to accurately recognize moving objects. The results indicate that our proposed method can effectively and accurately detect and recognize moving objects in image sequences.