Optimization of Moving Object Tracking Algorithm Based on Machine Vision
In response to the problem of slow target tracking speed in fully convolutional twin neural networks,an improved method of convolutional layer replacement is proposed based on the fully convolutional twin networks(SiamFC)algorithm.Replace the convolutional layer of the original algo-rithm accordingly to reduce the calculation of parameters during the convolution process,thereby impro-ving network speed and judgment.The research adopts a deep separable convolution and a mixed deep convolution for structural updates,and extracts image features through convolution kernels of different sizes.The study used a dataset to validate the improved algorithm.In the experiment,the one-time e-valuation success rate of the improved SiamFC algorithm was improved by about 2%compared to the SiamFC algorithm,and the accuracy was improved by about 3%.The success rate in spatial robustness has been improved by about 3%,and the accuracy has been improved by about 4%.The success rate in terms of time robustness has been improved by about 2%,and the accuracy has been improved by about 5%.The results indicate that the performance of the improved algorithm has been improved to a certain extent.