Activity recognition is significant in intelligent surveillance. In this paper, we present a novel approach to the recognition of interacting activities based on dynamic Bayesian network (DBN). In this approach the features representing the object motion are divided into two classes: global features and local features, which are at two different spatial scales. Global features describe object motion at a large spatial scale and relations between objects or between the object and environment, and local ones represent the motion details of objects of interest. We propose a new DBN model structure with state duration to model human interacting activities. This DBN model structure combines the global features with local ones harmoniously. The effectiveness of this novel approach is demonstrated by experiment.