Representation and recognition of human activities is an important problem for video surveillance and security applications. Considering the wide variety of settings in which surveillance systems are being deployed, it is necessary to create a common knowledge-base or ontology of human activities. Most current attempts at ontology design in computer vision for human activities have been empirical in nature. In this paper, we present a more systematic approach to address the problem of designing ontologies for visual activity recognition. We draw on general ontology design principles and adapt them to the specific domain of human activity ontologies. Then, we discuss qualitative evaluation principles and provide several examples from existing ontologies and how they can be improved upon. Finally, we demonstrate quantitatively in terms of recognition performance, the efficacy and validity of our approach for bank and airport tarmac surveillance domains.