Human behavior recognition based on multi-feature fusion of image
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Human behavior recognition has become one of the most active topics in computer vision and pattern recognition, which has a wide range of promising applications. In order to overcome the deficiency of single representation feature, a new recognition algorithm of human behavior based on multi-feature fusion of image and conditional random fields (CRF) is presented in this paper. The proposed algorithm consists of three essential cascade modules. First, AE features and RNN features were obtained by extracting the behaviors of the action by the recurrent neural network (RNN) and the AutoEncoder (AE), Then, feature similarity was introduced, the AE features and RNN features were fused to form a more comprehensive and accurate AE-RNN feature by using feature similarity. Finally, the multiple features were using for recognizing the human behavior of image by conditional random fields. The experimental results show that the proposed algorithm is effective and promising and has higher accurate recognition rate which can adapt to complex background and behavioral changes.
KeywordsBehavior recognition AutoEncoder Feature similarity Recurrent neuron networks Conditional random fields
This work was financially supported by the Key Technology Projects of Henan province of China under Grant 15210241004, Supported by Program for Changjiang Scholars and Innovative Research Team in University, the Key Technology Projects of Henan Educational Department of China under Grant 16A520036, the Key Technology Projects of Henan Educational Department of China under Grant 16B520001, the National Natural Science Foundation of China under Grant 41001251, Anyang science and technology plan project:Researches on Road Extraction Algorithm based on MRF for High Resolution Remote Sensing Image, and the Research and Cultivation Fund Project of Anyang Normal University under Grant AYNU-KP-B08.
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