Motion Recognition Based on Sum of the Squared Errors Distribution

Abstract

Human motion recognition is playing an increasingly important role in modern society. Direct recognition of complex motions has great limitations, so we usually study basic motions first. The key to the establishment of the basic motion set is how to determine the number of basic motions. Sum of the Squared Errors Distribution (SSED) is therefore proposed to determine the number of clustering classes of Self Organizing Maps (SOM). Secondly, the Weighted Tangent Segmentation (WTS) is also proposed to segment complex motions into simple ones, and the sequence with the time stamp is generated. Finally, Back Propagation with the time stamp (BPTS) is proposed to classify the simple motions according to the basic motion set, and the complex motion is recognized according to the time stamp. The motions of human upper limbs are used to verify the effectiveness of this method. SSED determines that the number of clusters of human upper limb motion is 10. The experimental results show that the correct segmentation rate (CSR) is 98.13%. The recognition rate of basic motions is 98.67% and 99.33% in user independent (UI) and user dependent (UD) experiments, respectively. The recognition rate of complex motions is 96.37%. Experiments on UI and UD recognitions verified the effectiveness of our algorithm. Compared with other recognition algorithms, the complex motion recognition method proposed in this article has better recognition performance.

Keywords

• Clustering algorithms
• multi-layer neural network
• object recognition
• object segmentation
• self organizing feature maps