In the field of intelligent transportation, background complexity, lighting changes, occlusion, and scale transformation affect the tracking results of moving vehicles in the video. We propose an improved vehicle object tracking algorithm based on Multi-Domain Convolutional Neural Networks (MDNet), combining the instance segmentation method with the MDNet algorithm, adding two attention mechanisms to the algorithm. The module extracts better features, ensures that the vehicle object adapts to changes in appearance, and greatly improves tracking performance. Our improved algorithm has a tracking precision rate of 91.8% and a success rate of 67.8%. The Vehicle Tracking algorithm is evaluated on the Object Tracking Benchmark (OTB) data set. The tracking results are compared with eight mainstream object tracking algorithms, and the results show that our improved algorithm has excellent performance. The object tracking precision rate and tracking success rate of this algorithm have achieved excellent results in many cases.

中文翻译：

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用于车辆跟踪的改进多域卷积神经网络方法

在智能交通领域，背景复杂度、光照变化、遮挡、尺度变换等都会影响视频中移动车辆的跟踪结果。我们提出了一种基于多域卷积神经网络（MDNet）的改进型车辆目标跟踪算法，将实例分割方法与MDNet算法相结合，在算法中增加了两种注意力机制。该模块提取了更好的特征，保证了车辆对象适应外观的变化，大大提高了跟踪性能。我们改进后的算法跟踪准确率为91.8%，成功率为67.8%。车辆跟踪算法在对象跟踪基准 (OTB) 数据集上进行评估。跟踪结果与八种主流目标跟踪算法进行对比，结果表明我们改进的算法具有优异的性能。该算法的目标跟踪准确率和跟踪成功率在很多情况下都取得了优异的成绩。