| dc.description.abstract | In recent years, human activity recognition(HAR) has been widely used in health-care, sports, baby and elderly monitoring and crime surveillance, etc. At present, Openpose is used by most people in HAR, because it has simple characteristics and does not A high-level camera is needed, and the joint point estimation can be achieved with ordinary 2D RGB images. It can not only detect the body joint, but also the hand and face joint points. Our study applies Openpose to the ATM withdrawal videos of moneymule and non-moneymule, and then through interviews with professional police officers, we can get the characteristics of the driver′s actions when withdrawing from the ATM. It including inserting a card, looking at the phone, looking around, and withdrawing money,and counting, etc. However, we convert the coordinate points obtained from the film into feature, and describe the action characteristics of ATM withdrawal in the form of feature. Also, because the action features occupy a low proportion in the video, the data is imbalance, so we use the deep learning model, bi-LSTM (Long Short-term memory model) to make evaluate in the way of n gram and undersampling, in order to pursue a higher precision and recall rate. | en_US |