While most video surveillance systems have centralized architectures that transmit videos to a central location for storage or real-time interpretation by human operators, a novel development of smart video sensors can significantly reduce the transmission load by extracting feature streams of videos that are enough for automatic semantic understanding. The overall video recognition processes are distributed into front-end sensors and back-end classifiers. Such distributed processing mechanism would significantly alleviate mundane or time-critical activities performed by human operators, and provide better network scalability. In this paper, we describe our implementation of smart video semantic sensors, which capture the context feature of the environments of the user. These sensors are used for recognizing the scenes, objects and events of the environment