This study attempts to recognize daily activities based on a wearable camera without using training data prepared by a user in her environment. Recently, deep learning frameworks have been publicly available, and we can now easily use deep convolutional neural networks (CNNs) pre-trained on a large image data set. In our method, we first detect objects used in the user's activity from her first-person images using a pre-trained CNN for object recognition. We then estimate an activity of the user using the object detection result because objects used in an activity strongly relate to the activity. To estimate the activity without using training data, we utilize knowledge on the Web because the Web is a repository of knowledge that reflects real-world events and common sense. Specifically, we compute semantic similarity between a list of the detected object names and a name of each activity class based on the Web knowledge. The activity class with the largest similarity value is the estimated activity of the user.