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The purpose of this paper is to present a rapid and efficient fish tracking method suitable for real world automatic underwater fish observation. Based on fish tracking, biologists are able to observe fish and their ecological environment. A distributed real-time underwater video stream system has been developed in Taiwan for large-scale, long-term ecological observation. In addition, not only does the system archive video data, but also incorporates data analysis. However, it is difficult to discriminate moving fish from drift water plants due to the severe drift of water plants caused by the water flow in real world underwater environments. Thus, fish tracking is complicated in unconstrained water. In order to overcome this problem, we propose a bounding-surrounding boxes method, which enables integration with state-of-the-art tracking methods for fish tracking in this paper. According to the method, fixing cameras must be used so that the moving fish are classified as foreground objects and are tracked, whereas the drifting water plants are classified as the background objects and are removed from the tracked objects. It enables the efficient, rapid removal of irrelevant information (non-fish objects) from large-scale fish video data. Experimental results show that the proposed method is able to achieve high accuracy

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