In this paper, we propose a distributed estimation algorithm for the detection of collective behaviors inmultiagent systems. Through local information exchange, the proposed algorithm can estimate the average of theintegrals of local time-varying behavior signals of individual agents, such as network moments, which could serveas a feature indicator for group collective behaviors. Under the assumption that the communication network amongagents is connected and bidirectional, the algorithm convergence is rigorously analyzed with an explicitly definedbounds of estimation errors. The event-triggering mechanism for information transmission is further employed forreducing the communication load. As a case study, the proposed distributed estimation algorithm is applied to detectthe collective behaviors of unicycle agents. Simulation results on the estimation of network centroids are providedto illustrate the effectiveness of the method.