计算机工程与应用2024，Vol.60Issue(5)：172-182,11.DOI:10.3778/j.issn.1002-8331.2211-0344
OMC框架下的行人多目标跟踪算法研究
Research on Pedestrian Multi-Object Tracking Algorithm Under OMC Framework
摘要
Abstract
Multi-object tracking is an important direction that has been widely studied in the field of computer vision,but in practical applications,the rapid movement of targets,lighting changes,and occlusions can lead to poor tracking perfor-mance,therefore,the multi-object tracking model OMC is used as the basic framework to carry out research to achieve further improvement of tracking performance.Firstly,to address the problem of uneven quality of target features in multi-object tracking,the feature extractor is optimized by integrating the GAM attention mechanism in the backbone net-work and replacing the upsampling method in the Neck network part.Secondly,to address the"competition problem"between detection and re-identification tasks in existing methods,a recursive cross-correlation network is constructed so that the model can learn the characteristics and commonalities of different tasks.Here,two sub-tasks are optimized sepa-rately,on the one hand,a new channel attention HS-CAM is designed to optimize the re-identification network;on the other hand,the boundary regression loss function of the detection part is replaced and the EIoU loss function is adopted.Experi-ments show that MOTA metrics can reach 73.5% ,IDF1 can reach 70.4% ,and MLgt is 11.7% on MOT16 dataset,which is 1.5 percentage points reduction compared to OMC algorithm.关键词
计算机视觉/多目标跟踪/GAM注意力机制/转置卷积/EIoU损失函数Key words
computer vision/multi-object tracking/GAM attention mechanism/transposed convolution/EIoU loss function分类
信息技术与安全科学引用本文复制引用
贺愉婷,车进,吴金蔓,马鹏森..OMC框架下的行人多目标跟踪算法研究[J].计算机工程与应用,2024,60(5):172-182,11.基金项目
国家自然科学基金(61861037) （61861037）
宁夏大学研究生创新研究项目(CXXM202223). （CXXM202223）
