Towards Context Address for Camera-to-Human Communication

Although existing surveillance cameras can identify people, their utility is limited by the unavailability of any direct camera-to-human communication. This paper proposes a real-time end-to-end system to solve the problem of digitally associating people in a camera view with their smartphones, without knowing the phones' IP/MAC addresses. The key idea is using a person's unique "context features", extracted from videos, as its sole address. The context address consists of: motion features, e.g. walking velocity; and ambience features, e.g. magnetic trend and Wi-Fi signal strengths. Once receiving a broadcast packet from the camera, a user's phone accepts it only if its context address matches the phone's sensor data.

We highlight three novel components in our system: (1) definition of discriminative and noise-robust ambience features;

(2) effortless ambient sensing map generation; (3) a context feature selection algorithm to dynamically choose lightweight yet effective features which are encoded into a fixed-length header. Real-world and simulated experiments are conducted for different applications. Our system achieves a sending ratio of 98.5%, an acceptance precision of 93.4%, and a recall of 98.3% with ten people. We believe this is a step towards direct camerato-human communication and will become a generic underlay to various practical applications.