The object tracking algorithm TransT, based on Transformer, achieves significant improvements in accuracy and success rate by fusing the extracted features of convolutional neural networks with the structure of Transformer. However, when dealing with the deformation of the object’s appearance, the algorithm exhibits issues such as insufficient tracking accuracy and drift, which directly affect the stability of the algorithm. In order to overcome this problem, this paper demonstrates how to expand scene information at the scale level during the fusion process and, on this basis, achieve accurate recognition and positioning. The predicted results are promptly fed back to the subsequent tracking process, from which temporal templates are embedded. Starting from both location and time can effectively improve the adaptive ability of the tracking model. In the final experimental comparison results, the algorithm proposed in this paper can adapt well to the situation of object deformation, and the overall performance of the tracking model has also been improved.

The TransT object tracking algorithm, built on Transformer architecture, effectively integrates deep feature extraction with attention mechanisms, thereby enhancing the stability and accuracy of the algorithm. However, this algorithm exhibits insufficient tracking accuracy and boundary box drift when dealing with similar background clutter, which directly affects the subsequent tracking process. To overcome this problem, this paper constructs a semantic enhancement model, which utilizes multi-layer feature representations extracted from deep networks, and correlates and fuses shallow features with deep features by using cross-attention. At the same time, in order to adapt to the changes in the surrounding environment of the object and establish good discrimination with similar objects, this paper proposes a dynamic mask strategy to optimize the attention allocation mechanism and finally employs an object template update mechanism to improve the adaptability of the model by comparing the spatio-temporal information of successive frames to update the object template in time, further enhancing its tracking performance in complex scenes. Experimental comparison results demonstrate that the algorithm proposed in this paper can effectively handle similar background clutter, leading to a significant improvement in the overall performance of the tracking model.