Improved Multi-domain Convolutional Neural Networks Method for Vehicle Tracking

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.