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Fisherposes for human action recognition using kinect sensor data

Ghojogh, B ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/JSEN.2017.2784425
  3. Abstract:
  4. This article proposes a new method for viewinvariant action recognition that utilizes the temporal position of skeletal joints obtained by Kinect sensor. In this method, the actions are represented as sequences of several pre-defined poses. After pre-processing, which includes skeleton alignment and scaling, the appropriate feature vectors are obtained for recognizing and discriminating the pose of every frame by the proposed Fisherposes method. The proposed regularized Mahalanobis distance metric is used in order to recognize both the involuntary and highly made-up actions at the same time. Hidden Markov Model (HMM) is then used to classify the action related to an input sequence of poses. For taking into account the motion in the actions which are not separable by solely their temporal poses, histograms of trajectories are also proposed. The proposed action recognition method is capable of recognizing both the voluntary and involuntary actions, as well as posebased and trajectory-based ones with a high accuracy rate. The effectiveness of the proposed method is experimented on three publicly available datasets, TST fall detection, UTKinect, and UCFKinect datasets. IEEE
  5. Keywords:
  6. Hidden markov model (HMM) ; Linear discriminant analysis (LDA) ; Skeleton ; Three-dimensional displays ; Trajectory ; Discriminant analysis ; Feature extraction ; Graphic methods ; Markov processes ; Musculoskeletal system ; Sensors ; Trajectories ; Trellis codes ; Fisher ; Fisherpose ; Histogram ; Human-action recognition ; Linear discriminant analysis ; Skeleton data ; Three-dimensional display ; Windowing
  7. Source: IEEE Sensors Journal ; 2017 ; 1530437X (ISSN)
  8. URL: http://ieeexplore.ieee.org/document/8219411