Generally, the measurement of three-dimensional (3D) swimming behavior in zebrafish relies on commercial software or requires sophisticated scripts, and depends on more than two cameras to capture the video. Here, we establish a simple and economic apparatus to detect 3D locomotion in zebrafish, which involves a single camera capture system that records zebrafish movement in a specially designed water tank with a mirror tilted at 45 degrees. The recorded videos are analyzed using idTracker, while spatial positions are calibrated by ImageJ software and 3D trajectories are plotted by Origin 9.1 software. This easy setting allowed scientists to track 3D swimming behavior of multiple zebrafish with low cost and precise spatial position, showing great potential for fish behavioral research in the future.Keywords: 3D locomotion; behavior; zebrafish; idTracker; ImageJ Zebrafish is well-known as an ideal experimental animal model in biomedical research, especially in the fields of developmental and genetic studies and drug discovery approaches [1][2][3][4]. It has become widely used within the field of pharmaceutical research and toxicology, in which ideally thousands of chemicals can be screened rapidly in vivo for therapeutic and toxic potential that are related to human disease susceptibility and risk [5]. In addition, zebrafish is also an excellent model for behavioral research because of their consistency and the rational refection of their mental and physical changes to new environments, and a comparable neural circuit system with high vertebrate counterparts [6][7][8]. Therefore, zebrafish has emerged as a promising new organism for research on anxiety due to their robust cortisol stress response, behavioral strain differences, and sensitivity to drug treatments or predators as well as their change in alarm pheromones [9][10][11][12]. There are various models commonly used to assess zebrafish behavior, include shoaling test [13][14][15], social preference [16,17], light-dark box [18][19][20], open-field [21][22][23], and novel tank [24,25] models. Among these models, they required accurate, reliable, and reproducible detection of the subject's spatiotemporal location. Previously, the manual quantification of animal behavior may have suffered systematic errors, leading to data misinterpretation [26]. In contrast, computational video-tracking technologies can record and analyze movements and optimize observation on multiple behavioral endpoints to reduce internal influence [27]. Moreover, another advantage of using the video-tracking approach is its ability to repetitively store, replay, and analyze recorded videos, instead of observing every behavioral endpoint with only human eyes [28,29].Based on the position of fish, computer-vision tracking can be classified into two-dimensional (2D) and three-dimensional (3D). Even though 2D tracking was feasible to analyze fish behavior,