Human behavior recognition based on multi-feature fusion of image

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.

[1]  Qian Wang,et al.  Vision-based behavior prediction of ball carrier in basketball matches , 2012 .

[2]  Hani Hagras,et al.  A fuzzy logic-based system for the automation of human behavior recognition using machine vision in intelligent environments , 2015, Soft Comput..

[3]  Mykel J. Kochenderfer,et al.  Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior , 2017, IEEE Transactions on Intelligent Transportation Systems.

[4]  Richard P. Wildes,et al.  Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Zhaohui Peng,et al.  Extracting Web Entity Activities Based on SVM and Extended Conditional Random Fields: Extracting Web Entity Activities Based on SVM and Extended Conditional Random Fields , 2012 .

[6]  Juergen Gall,et al.  A bag-of-words equivalent recurrent neural network for action recognition , 2017, Comput. Vis. Image Underst..

[7]  Jiwen Lu,et al.  Single Sample Face Recognition via Learning Deep Supervised Autoencoders , 2015, IEEE Transactions on Information Forensics and Security.

[8]  Mori Fumihiko,et al.  Color Image Segmentation based on Statistics of Location and Feature Similarity , 2011 .

[9]  Yi Li,et al.  Real-time oriented behavior-driven 3D freehand tracking for direct interaction , 2013, Pattern Recognit..

[10]  Chalavadi Krishna Mohan,et al.  Classification of human actions using pose-based features and stacked auto encoder , 2016, Pattern Recognit. Lett..

[11]  Dmitry B. Goldgof,et al.  Understanding Transit Scenes: A Survey on Human Behavior-Recognition Algorithms , 2010, IEEE Transactions on Intelligent Transportation Systems.

[12]  Ling Shao,et al.  Silhouette Analysis-Based Action Recognition Via Exploiting Human Poses , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[13]  Carlos Alberto Silva,et al.  Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields , 2016, Journal of Neuroscience Methods.

[14]  Kang Ryoung Park,et al.  Fuzzy system based human behavior recognition by combining behavior prediction and recognition , 2017, Expert Syst. Appl..

[15]  程学旗,et al.  Distributed Stochastic Gradient Descent with Discriminative Aggregating , 2015 .