Fuzzy rules based indoor human action recognition using multi cameras

In this paper, we propose a method for recognizing human actions indoors using fuzzy rules and multi cameras. To recognize the human actions, initially, we use the background difference method to extract human area candidates. We then extract HOG features and learn to detect humans using the features and AdaBoost. Fuzzy rules are then used of detect the human actions. The detected human is determined to be stationary or not using the distance between the detected areas in consecutive frames. We also estimate the direction the human is facing using the width of detection, and finally recognize the standard action using the height of the detected region. In addition, we recognize suspicious action using duration of detection and presence of abandoned object. After experiments, recognition accuracy achieved for “walking” and “stop” actions is about 87%, for “running” action about 54%, for “sitting” about 96%, for “desk working” about 83%, and “falling” about 88%.