Human action recognition (HAR) in videos is a challenging problem in computer vision and pattern recognition with wide applications.Most works on HAR have been visible-spe ctrum oriented.However,this paper uses thermal infrared imaging for HAR.In order to overcome the deficien cy of single scale and individual representation method of action on HAR,a new recognition algorithm of human act ion using dense trajectories-based multi-feature fusion is presented.Our method consists of th e following steps:The dense trajectories (DTs) of the input action video are obtained by using dense samplin g ;Three dense trajectories-based descriptors are constructed,namely histogram of oriented gradient (HOG), histogram of optical flow (HOF) and motion boundary histograms (MBH);The fusion feature is constructed by u sing the popular bag-of-features (BoF) representation of HOG,HOF and MBH,respectively.And fr om the pattern recognition perspective,action recognition can commonly be viewed as a multiclass classific ation problem.Consequently,a k-NN classifier is employed to recognize the human acti on using the computed d ense trajectories-based fusion features.The intensive experimental results show that the proposed method can a chieve the correct recognition rate above 96.67% on the benchmark infrared action dataset of IADB.Interrelated analy ses conclude that the proposed algorithm is effective and promising for visible and infrared human action recognition.