Lower-Dimensional Feature Sets for Template-Based Motion Recognition Approaches

Abstract

Problem statement: In template-based motion recognition approaches, feature sets are computed from the template for classification. Hu invariants are widely employed for this purpose since its inception. However, development of lower-dimensional feature vector sets is required for faster computation along with robust recognition. The concept of reduced size of Hu moment is really interesting. From its inception, seven higher orders Hu moments have been employed by many researchers without considering why seven and why not less numbers. Approach: In this study, we analyzed with various feature sets with different number of Hu moments and rationalized that based on the characteristics of central moments, it is not necessary to employ all the seven moments in every applications and, in that way, we can reduce the computational cost and make it faster. Results: Based on various feature vectors sets, it is evident that we can use lower dimensional feature vectors for our Directional Motion History Image (DMHI) method and other methods. Conclusion: Therefore, we can conclude that we do not need all seven invariants, rather 1st two or three invariants seem enough-as we are not reproducing the image. Higher invariants are noisy and hence can be ignored. The 0th order moment for Energy images provide enough information about the mass area and hence, no need to calculate the other seven invariants.