**6. Conclusion**

In this chapter we presented the approach developed for the human action recognition using spatiotemporal interest points STIPs. The STIPs were detected by the application of Laptev STIPs detector. Our classification approach is based on a parameter vector deduced from different studies. The first concerns STIPs number in 100 frames, the second studies the evolution of this number in each frame of the sequence while the third classifies the STIPs in spatiotemporal boxes associated to different parts of the body. For classification we used the k-means classifier. The approach developed has leaded to good performances compared to the well known methods for human action recognition.

As we have only considered K-means as the classification algorithm, we are actually implementing SVM and pLDA algorithms and we plane to make a comparative study between them. Additionally, other metrics will be used to evaluate the methods performances such as Precision, Recall, True Negative Rate (TNR) etc.
