Abstract: Human motions always contain only motions of some body parts, but much of the existing
methods on action recognition don’t take the motion intensity of each joint into account, which lower
the accuracy of action recognition in some extent. To solve this problem, an adaptive joint weighting
scheme is proposed to calculate the weight of each joint and combined the weights with dynamic time
warping (DTW) to recognize actions. Firstly, the action sequence was segmented into several
segments and some most violent joints in each segment are assigned higher weight while the
remaining joints are evenly weighted. Then feature vectors of two action sequences were extracted and
the distance between two action sequences were calculated by DTW. Finally the action recognition was
achieved by K-nearest neighbor method. The experiments showed that the overall classification
accuracy of the proposed method is higher, and the result is also good for some similar actions.

Key words: action recognition, human motion analysis, dynamic time warping, joint weight, pose
feature