ABSTRACT
We propose a novel framework for searching for people in surveillance videos. Rather than relying on face-recognition technology, which is known to be sensitive to typical surveillance conditions such as lighting changes, face pose variations, and low-resolution imageries, we approach the problem in a different way: we search for people based on a parsing of human parts and their attributes, including facial hair, eyewear, clothing color, etc. These attributes can be extracted using detectors learned from large amounts of training data. A complete system that implements our framework is presented. At the interface, the user can specify a set of personal characteristics, and the system then retrieves events that match the provided description. For example, a possible query is “show me the bald people who entered a given building last Saturday wearing a red shirt and sunglasses.” This capability is useful in several applications, such as fi nding suspects or missing people. We also present experiments on surveillance video, which demonstrate the benefi ts of our approach.
