Surveillance cameras may be a tool for solving crimes, but what about using them to prevent or stop criminal and terrorist acts? That calls for someone or some "thing" to keep an eye on the video feeds 24-7.

While computerized monitoring would seem to be the obvious answer, creating software programs that can recognize suspicious activities or suspect individuals has proven highly difficult -- a solution that Rama Chellappa, a professor in the department of electrical and computer engineering of the University of Maryland's A. James Clark School of Engineering, is hoping to provide through the development of a real-time computer monitoring system using artificial intelligence.

A pioneer in the development of pattern recognition and computer vision software, Chellappa and his research assistants have developed a compact, digital signature for characterizing human gait and corresponding activities, such as humans carrying objects like backpacks, handbags, or briefcases, using video data from digital surveillance cameras and corresponding algorithms.

When a person's limbs are unencumbered, gait movements are symmetrical. Represented graphically, these movements form a twisted helical pattern resembling a "figure 8" called a double helical signature.

Chellappa and his team call this pattern, which is slightly different in each individual, "human gait DNA."

An individual's gait pattern is changed by any activity that changes the symmetry of the movements, such as carrying a package. By defining these signatures, the system can recognize unique patterns in human gait and automatically detect asymmetric movements like an individual walking with a hidden object tied to an ankle or wrist. Hidden objects secured to the body in ways that don't affect movement symmetry, for example, a fanny pack that is belted around the waist, aren't currently detected by this technology.

Chellappa and his team have integrated human gait DNA into a real-time video surveillance system and used it to study and locate pedestrians. The experimental results have demonstrated the effectiveness of the system under lighting changes, shadows, camera motion, various viewing angles, as well as significant obstacles in the cameras' viewing angles. The results also indicate that the approach is superior to many existing methods in terms of accuracy and reliability.