Human Activity Recognition Based on Loss-Net Fusion Domain Convolutional Neural Networks

Human activity recognition is now a hot issue in artificial intelligence research. The purpose of activity recognition is to analyze behaviors in an unknown video or image sequence by computer. Unlike previous static recognition, the challenge of behavior recognition is how to capture motions between still frames. For reports of Convolutional Neural Network(CNN) architecture in the past, we used a method of implicitly capturing motion information between adjacent frames to improve the CNN architecture, taking the original video frames as input and predicting the action class without explicit optical action class calculation directly. Our architecture was trained and tested using videos from the UCF-101 human behavior database and achieved very ideal results.

[1]  Zhe Wang,et al.  Towards Good Practices for Very Deep Two-Stream ConvNets , 2015, ArXiv.

[2]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[3]  Cordelia Schmid,et al.  Dense Trajectories and Motion Boundary Descriptors for Action Recognition , 2013, International Journal of Computer Vision.

[4]  Limin Wang,et al.  Bag of visual words and fusion methods for action recognition: Comprehensive study and good practice , 2014, Comput. Vis. Image Underst..

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

[6]  Lorenzo Torresani,et al.  Learning Spatiotemporal Features with 3D Convolutional Networks , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).

[7]  Mubarak Shah,et al.  UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild , 2012, ArXiv.

[8]  Michael Unser,et al.  Convolutional Neural Networks for Inverse Problems in Imaging: A Review , 2017, IEEE Signal Processing Magazine.

[9]  Cordelia Schmid,et al.  P-CNN: Pose-Based CNN Features for Action Recognition , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[10]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Cordelia Schmid,et al.  Action Recognition with Improved Trajectories , 2013, 2013 IEEE International Conference on Computer Vision.