Mostofi "Video ID Through Walls"
"Your Video Can ID You Through Walls"
This novel video-WiFi cross-modal gait-based person identification system, which they refer to as XModal-ID (pronounced Cross-Modal-ID), could have a variety of applications, from surveillance and security to smart homes. For instance, consider a scenario in which law enforcement has a video footage of a robbery. They suspect that the robber is hiding inside a house. Can a pair of WiFi transceivers outside the house determine if the person inside the house is the same as the one in the robbery video? Questions such as this have motivated this new technology.
“Our proposed approach makes it possible to determine if the person behind the wall is the same as the one in video footage, using only a pair of off-the-shelf WiFi transceivers outside,” said Yasamin Mostofi. “This approach utilizes only received power measurements of a WiFi link. It does not need any prior WiFi or video training data of the person to be identified. It also does not need any knowledge of the operation area.”
The proposed methodology and experimental results will appear at the 25th International Conference on Mobile Computing and Networking (MobiCom) on October 22. The project was funded by a pair of grants from the National Science Foundation that focus on through-wall imaging and occupancy assessment.
In the team’s experiments, one WiFi transmitter and one WiFi receiver are behind walls, outside a room where a person is walking. The transmitter sends a wireless signal whose received power is measured by the receiver. Then, given video footage of a person from another area — and by using only such received wireless power measurements — the receiver can determine whether the person behind the wall is the same person seen in the video footage.
This innovation builds on previous work in the Mostofi Lab, which has pioneered sensing with everyday radio frequency signals such as WiFi since 2009.
“However, identifying a person through walls, from candidate video footage, is a considerably challenging problem,” said Mostofi. Her lab’s success in this endeavor is due to the new proposed methodology they developed.
“The way each one of us moves is unique. But how do we properly capture and compare the gait information content of the video and WiFi signals to establish if they belong to the same person?”
The researchers have proposed a new way that, for the first time, can translate the video gait content to the wireless domain.