Vertical slot fishways are hydraulic structures which allow the upstream migration of fish through obstructions in rivers. The appropriate design of these devices should take into account the behavior and biological requirements of the target fish species. However, little is known at the present time about fish behavior in these artificial conditions, which hinders the development of more effective fishway design criteria. In this work, an efficient technique to study fish trajectories and behavior in vertical slot fishways is proposed. It uses computer vision techniques to analyze images collected from a camera system and effectively track fish inside the fishway. Edge and region analysis algorithms are employed to detect fish in extreme image conditions and Kalman filtering is used to track fish along time. The proposed solution has been extensively validated through several experiments, obtaining promising results which may help to improve the design of fish passage devices.