Recognition of human walking/running actions based on neural network

High precision recognition of human actions directly from video records is still open problem. In this paper an approach for human action recognition for full body analysis based on a novel configuration of convolutional neural network is presented. The proposed convolutional neural network approach is a variant of multilayer perceptron, which main advantage is its ability to learn the feature extraction layers during retropropagation of errors from the lower layers using as input an image without any pre-processing. It permits to introduce the human descriptive action extraction process directly to neural network for more fast recognition. In order to evaluate the proposed approach a framework for recognizing human walking/running actions has been designed and tested on developed dataset that consists of multiple video records providing 4000 images per activity used for motion detection and activity interpretation.

[1]  Katsuhiko Mori,et al.  Facial expression recognition combined with robust face detection in a convolutional neural network , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[2]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[3]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[4]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[5]  Chunheng Wang,et al.  Learning weighted features for human action recognition , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[6]  Paulo Peixoto,et al.  On Exploration of Classifier Ensemble Synergism in Pedestrian Detection , 2010, IEEE Transactions on Intelligent Transportation Systems.

[7]  Alexandros Iosifidis,et al.  View-Invariant Action Recognition Based on Artificial Neural Networks , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Tae-Seong Kim,et al.  Silhouette-based Human Activity Recognition Using Independent Component Analysis, Linear Discriminant Analysis and Hidden Markov Model , 2010 .

[9]  Henk Corporaal,et al.  Efficiency Optimization of Trainable Feature Extractors for a Consumer Platform , 2011, ACIVS.

[10]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering , 2006 .

[11]  Simon Haykin,et al.  GradientBased Learning Applied to Document Recognition , 2001 .

[12]  Sameer Singh,et al.  Novelty detection: a review - part 2: : neural network based approaches , 2003, Signal Process..

[13]  Yan Meng,et al.  Human activity detection using spiking neural networks regulated by a gene regulatory network , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[14]  M. Szarvas,et al.  Real-time Pedestrian Detection Using LIDAR and Convolutional Neural Networks , 2006, 2006 IEEE Intelligent Vehicles Symposium.

[15]  N. M. Charkari,et al.  Lying human activity recognition based on shape characteristics , 2012, 2012 2nd International eConference on Computer and Knowledge Engineering (ICCKE).

[16]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Dariu Gavrila,et al.  An Experimental Study on Pedestrian Classification , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Hairong Qi,et al.  Feature Extraction and Representation for Distributed Multi-View Human Action Recognition , 2013, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.