Abstract: To address the challenges of appearance similarity, weak visual features, and frequent occlusion in bird flock imagery, this paper proposes an anti-occlusion tracking algorithm based on spatiotemporal topological feature. A binary spatiotemporal descriptor is constructed to encode the relative positional feature between individuals and their neighbours, overcoming the limitations of appearance-based features. An occlusion reasoning module integrated with multi-view geometric constraint is designed. It adopts a sliding window trajectory enumeration and optimal assignment strategy, effectively resolving the issue of trajectory identity ambiguity caused by occlusion. The results indicate that the proposed descriptor achieves a discriminability (Kullback-Leibler divergence) of 2.18 ~ 3.95, outperforming traditional and deep features. The proposed 2D tracking algorithm attains a multi-object tracking accuracy (MOTA) of 93.5% ~ 96.2%, while the 3D tracking algorithm elevates MOTA to 98.3%.