This paper presents the groundwork for a distributed network of collaborating, intelligent surveillance cameras, implemented with low-cost embedded microprocessor camera modules. Each camera trains a person detection classifier using the Winnow algorithm for unsupervised, online learning. Training examples are automatically extracted and labelled, and the classifier is then used to locate person instances. To improve detection performance, multiple cameras with overlapping fields of view collaborate to confirm results. We present a novel, unsupervised calibration technique that allows each camera module to represent its spatial relationship with the rest. During runtime, cameras apply the learned spatial correlations to confirm each other's detections. This technique implicitly handles non-overlapping regions that cannot be confirmed. Its computational efficiency is well-suited to real-time processing on our hardware.
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