Convolutional neural random fields for action recognition. (November 2016)

• Record Type: Journal Article
• Authors: Liu, Caihua; Liu, Jie; He, Zhicheng; Zhai, Yujia; Hu, Qinghua; Huang, Yalou
• Is Part Of: Pattern recognition. Volume 59(2016:Nov.)
• Page Start: 213
• Page End: 224
• Publication Date: 2016-11
• Subjects: Action recognition -- Sequence labeling -- Conditional random fields -- Spatio-temporal convolution; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2016.03.019
• Languages: English
• ISSNs: 0031-3203

Abstract

Abstract: A deep discriminative structured model, convolutional neural random fields (CNRFs), is proposed for action recognition problem. In the proposed model, a spatio-temporal convolutional neural network (CNN) is developed for invariant feature learning from raw input frames, and the CNN is combined with conditional random fields (CRFs) for capturing the interdependencies between outputs. The parameters from both CRF and CNN are learned in a joint fashion which enables structured prediction and feature learning as well. We also explore different combinations of observation and transition feature functions based on the learned high level features from convolution part. The approach enjoys the advantages of both CNN and CRF, it has the invariant feature learning ability possessed by the former and structured prediction ability of the latter. The experimental results on both segmented and unsegmented human action recognition datasets show that CNRF boosts the performance over the comparison methods by a large margin. Abstract : Highlights: We develop a novel spatio-temporal CNN architecture for feature learning from video frames. CRF is coupled with CNN to achieve feature learning and structured prediction simultaneously. We explore different combinations of feature functions for sequence labeling. We validate our framework on segmented and unsegmented action datasets, respectively.