Advanced Internet of Things (IoT) techniques have made human-environment interaction much easier. Existing solutions usually enable such interactions without knowing the identities of action performers. However, identifying users who are interacting with environments is a key to enable personalized service. To provide such add-on service, we propose WTW (who takes what), a system that identifies which user takes what object. Unlike traditional vision-based approaches, which are typically vulnerable to blockage, our WTW combines the feature information of three types of data, i.e., images, skeletons and IMU data, to enable reliable user-object matching and identification. By correlating the moving trajectory of a user monitored by inertial sensors with the movement of an object recorded in the video, our WTW reliably identifies a user and matches him/her with the object on action. Our prototype evaluation shows that WTW achieves a recognition rate of over 90% even in a crowd. The system is reliable even when users locate close by and take objects roughly at the same time.