Fish behavior data are quite important to water pollution monitoring and these kinds of data sets imply the fish healthy conditions. To acquire fish behavior data, we present a multiple-view based fish swimming 3D trajectory reconstruction technique. In this technique, we use three cameras to observe an aquarium from three different aspects and employ foreground detection and tracking methods to reconstruct 2D fish swimming trajectories from these three cameras. Based on the multiple-view 3D reconstruction theories, we fuse these three 2D fish swimming trajectories to achieve a 3D fish swimming trajectory. Experiments show that our method can accurately reconstruct the 3D swimming trajectory of a single fish in a cubic aquarium and get the real-time 3D position and the swimming speed of the fish. This method needs neither expensive experimental equipment nor strict installation requirement to make cameras vertically face the aquarium faces. However, it can acquire accurate 3D fish swimming trajectories in real-time.