| When a compartment fire, it is impossible to monitor the safety of pedestrian effectively, a method of pedestrian’s fine grained behavior recognition based on built in sensors of smartphone was proposed. In this method, the multi sensors of mobile phone were used to collect the data of the pedestrian’s characterization. After detecting the abnormal sub sequence, the feature vectors were extracted. Then, the algorithm of Key DTW and the models of classifying were respectively used to recognize and understand the pedestrian’s activities. Next, comparing the ability of classifying in different position of device and in various combination of smartphone sensor. Finally, analyzing the pedestrian’s current status of physiological, psychological and positional. The method will provide much valuable information for the rescue operation. The results of experiments showed that the method has higher accuracies and efficiencies of activity recognition.