Human activity recognition based on weighted limb features

Human activity recognition plays an important role in personal assistive robot, being able to recognize human activity and perform corresponding assistive action is a great challenges for personal assistive robot. Human body is an articulated system of rigid segments that can be divided into five parts, but many existing methods always identify actions based on the motion trajectories of whole body. In this paper, taking into account the fact that most actions can be performed by a few limbs and the other limbs should not impact on the action recognition, we proposed an activity recognition method based on limb weights. The weight of each limb is composed of consistency weight and uniqueness weight, which are learned according to the similarity degree among different sequences for each specific action. The covariance descriptor, which is the concatenation of eigenvalues extracted from covariance matrices, is adopted to represent the motion trajectory of each limb. In order to distinguish action instances from each other in the feature sequences, a simple annotation method is used. Experimental results on the Cornell activity dataset and the Lab dataset show that the proposed method not only can outperform the state-of-the-art algorithms, but also is appropriate to recognize the actions whose non-core limbs' trajectories are different from each other.

Authors

• Liang Zhang 0010
• Wenhan Yang
• Guangming Zhu 0001
• Peiyi Shen
• Juan Song

Keywords

• Covariance matrices
• Training
• Trajectory
• Eigenvalues and eigenfunctions
• Integrated circuits
• Hidden Markov models
• Action Recognition
• Human Activity Recognition
• Covariance Matrix
• Sequence Features
• Motion Trajectory
• Action Instances
• Drinking Water
• Eigenvectors
• Sequence Of Actions
• Precision And Recall
• 3D Space
• Symmetric Matrix
• Left Arm
• Left Leg
• Human Motion
• Recognition Results
• Sensor Accuracy
• Atomic Motion
• Human Pose
• Limb Length
• Skeletal Data
• RGB-D Sensor
• RGB Information
• Limb Motion