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In recent years, the application of information technology in sheep farming has become increasingly sophisticated, necessitating more accurate individual identification and behavior monitoring. This, in turn, has placed higher demands on the accuracy of multiple object tracking (MOT) algorithms, which formed the foundation of these applications. However, existing MOT algorithms often underperformed in scenarios involving object occlusion and dynamic environments. Two novel tracking cues, depth modulated IoU (DIoU) and tracklet direction modeling (TDM), was proposed, aiming at enhancing the precision and robustness of multiple object tracking by supplementing the intersection over union (IoU) cue. DIoU improved the traditional IoU calculation by incorporating depth information of the objects. TDM focused on the movement trends of targets, predicting their future directions based on their historical movement patterns. The DIoU and TDM strategies were integrated into the BoT-SORT algorithm, resulting in an improved multiple object tracking algorithm. Evaluations on two datasets showed that the enhanced algorithm increased the multiple object tracking accuracy (MOTA) by 1.6 percentage points and 1.7 percentage points and the identification F1 score (IDF1) by 1.9 percentage points and 1.0 percentage points, respectively, compared with baseline methods. These results indicated that the improved algorithm significantly enhanced tracking continuity and accuracy in complex scenarios. This research provided insights and methods for multiple object tracking technology, holding significant implications for practical applications.