# Object Tracking Algorithm Based on Multi-Layer Feature Fusion and Semantic Enhancement
## 3 Experimental Results and Analysis  
### Experimental Environment and Settings  
In this research, the experimental foundation was laid using the Ubuntu 20.04.5 operating system, with the PyTorch 1.13.1 deep learning framework constructed upon it. For the development and training of the network model, the Python 3.9 programming language was predominantly employed, while model testing was collaboratively executed using Python and MATLAB. Throughout the experimental process, a suite of development modules, including opencv-python, pycocotools, pandas, torch, pytz, tqdm, and torchvi  
Figure 6: The object template update module. (The green, blue, and brown modules correspond to the feature vectors of the frames \(t-1\), \(t\), and \(t-2\), respectively. Meanwhile, the gray module signifies the feature vector output by the IOU prediction module.).  
sion, were utilized to facilitate the implementation of various functionalities. The primary software involved was PyCharm-community-2022.3.3 and MATLAB 2020b.  
The key specifications of the computer utilized in the experiment were as follows: It was equipped with the 12 th Gen Intel (R) Core (TM) i7-12700 processor (Intel Corporation, Santa Clara, CA, USA), an NVIDIA GeForce RTX 3080 Ti graphics card (NVIDIA Corporation, Santa Clara, CA, USA), and 32 GB of memory. Given the constraints of available equipment, these hardware configurations robustly supported the experimental operations, ensuring the smooth execution of deep learning model training and testing. They met the fundamental computational performance requirements of this research and contributed to the acquisition of precise and dependable experimental results.  
In terms of model training, this paper adopted a similar strategy with TransT, which chose AdamW [24] as the optimizer. The specific training process was divided into two stages:  
The first stage: We concentrated on honing the network backbone of Transtfpn-Mask. In TransT's initial setup, the backbone's learning rate was set at 1 \(\times\) 10\({}^{-5}\), other parameters' learning rate at 1 \(\times\) 10\({}^{-4}\), and weight decay at 1 \(\times\) 10\({}^{-4}\), and the batch size was 38. The training spanned 1000 epochs, with each epoch comprising 1000 iterations. After hitting the 500-epoch mark, the learning rate was reduced to a tenth of its initial value. Given the constraints of our current equipment, we mirrored the main learning rate at 1 \(\times\) 10\({}^{-5}\), other parameters' learning rate at 1 \(\times\) 10\({}^{-4}\), and weight decay at 1 \(\times\) 10\({}^{-4}\). However, the batch size was capped at 6. We could only train for a total of 665 epochs, with each epoch still undergoing 1000 iterations. This is because the model was found to converge at around 665 epochs, indicating that further training would not significantly improve performance. The learning rate similarly dropped to a tenth of its original value after 500 epochs.  
The second stage: Building on the Transtfpn-Mask foundation, we froze all weights of its trunk and shifted the training focus to the IoU prediction head. The training dataset remained consistent with the first stage. Here, the learning rate was cranked up to 1 \(\times\) 10\({}^{-3}\), weight decay stayed at 1 \(\times\) 10\({}^{-4}\), and the batch size was set at 24. We trained for a total of 150 epochs, with each epoch consisting of 200 iterations. Once we hit the 50-epoch milestone, the learning rate was slashed by a factor of 10.  
Regarding the equipment performance, this paper finally realized that TransT's effect compared with the original was slightly inferior, but to emphasize, for the processes of both the emergent TransT and the other algorithm, this paper adopted the same experimental environment, training datasets, and parameters. Therefore, the TransT effect used for comparison in this paper was based on the results after they were reproduced.