There is an increasing need for the development of supportive technology for elderly people living independently in their own homes, as the percentage of elderly people grows. A crucial issue is resolving conflicting goals of providing a technology-assisted safer environment and maintaining the users' privacy. We address the issue of recognizing ordinary household activities of daily living (ADLs) by exploring different sensing modalities: multi-view computer-vision based silhouette mosaic and radio-frequency identification (RFID)-based direct sensors. Multiple sites in our smart home testbed are covered by synchronized cameras with different imaging resolutions. Training behavior models without costly manual labeling is achieved by using RFID sensing. Privacy is maintained by converting the raw image to granular mosaic, while the recognition accuracy is maintained by introducing the multi-view representation of the scene. Advantages of the proposed approach include robust segmentation of objects, view-independent tracking and representation of objects and persons in 3D space, efficient handling of occlusion, and the recognition of human activity without exposing the actual appearance of the inhabitants. Experimental evaluation shows that recognition accuracy using multi-view silhouette mosaic representation is comparable with the baseline recognition accuracy using RFID-based sensors.