This paper describes a prototype system for performing handover between cameras with non-overlapping views. The design is being used to identify problems that may arise in the development of a larger, more capable, and fully automatic system. If there is no information about the spatio-temporal relationship between cameras to assist in matching individuals, similarities in appearance may be used. Here, the object's appearance is represented by a vector of features calculated from its delineation. The features considered are the scale-invariant feature transform, grey-level co-occurrence matrix features, local binary patterns, Zernike moments and some simple colour features. The system has been tested on a difficult surveillance scenario, which considers opposing views of the subjects (frontal presentation in one sequence matched with rear presentation in the other, and vice versa). Several classification strategies are employed to determine the best match across presentations of the subjects in each sequence. The quality of the results was lower than expected but provides useful information for future robustification of the system.
['Nicholas J. Redding', 'Julius Fabian Ohmer', 'Judd Kelly', 'Tristrom Cooke']
Cross-Matching via Feature Matching for Camera Handover with Non-overlapping Fields of View