This poster presents the most recent deployment of wireless Cyclops camera nodes in the nest boxes of the James Reserve, and the various vision techniques used to analyze the acquired data. This has been the largest deployment of Cyclops cameras to date, and the various vision techniques that this poster presents have been constructed to take full advantage of the benefits of this wireless camera system. Various techniques such as patch and macro-block differencing using Bhattacharyya distances and root mean square values, corner/edge point detection, and background modeling using Gaussian mixture models have been incorporated into this vision system in ways which strive to increase the accuracy of the entire system. This approach to recognize and robustly identify different avian activities can work remarkably, achieving as high as 95% accuracy.