Human activity recognition based on morphological dilation followed by watershed transformation method

Efficiency and accuracy are the most important terms for human activity recognition. Most of the existing works have the problem of speed. This paper proposed an efficient algorithm to recognize the activities of the human. There are three stages of this paper, segmentation, feature extraction and recognition. In this paper our contribution is in segmentation stage (based on morphological dilation) and in feature extraction stage (using watershed transformation). The proposed algorithm has been tested on six different types of activities (containing 420 frames). The recognition performance of our method has been compared with the existing method using Principle Component Analysis (PCA) to derive activity features. The results of our proposed method are comparable with the existing work. But in-term of efficiency, our algorithm was much faster than the existing work. The average accuracy and efficiency of the proposed algorithm for recognition was 80.83 % and 302.2 ms respectively.