基于深度学习的目标跟踪算法将卷积深层输出结果作为特征,虽然准确度高但耗时长;基于融合特征的目标跟踪算法按照响应值融合目标特征,虽然跟踪速度快,但降低了准确度。为了兼顾目标跟踪算法的时效性和准确度,提出基于相似性特征估计的目标跟踪算法。首先利用重要性重采样滤波粒子构建目标观测模型,其中包括选择粒子状态、转移系统状态、构建观测模型、粒子权值更新以及重采样过程。在此基础上,提取目标的统计纹理特征、运动尺寸特征以及运动速度与方向特征,并融合目标特征构建目标特征框架。结合相似性特征估计完成目标定位,包括描述目标模型、表示候选模型、度量目标具体相似度以及目标定位过程。在完成目标定位后,基于实时压缩实现目标跟踪。本文算法的跟踪准确度均在90%以上,跟踪过程耗时保持在450ns以下,性能优于基于深度学习和融合特征的目标跟踪算法。本文算法能够快速、准确实现对目标的跟踪,应用优势较强。

The target tracking algorithm based on deep learning takes the deep convolution output as the feature, which is high in accuracy but time-consuming. The target tracking algorithm based on fusion features fuses the target features according to the response value, although the tracking speed is fast, but the accuracy is reduced. In order to consider the timeliness and accuracy of the target tracking algorithm, a target tracking algorithm based on similarity feature estimation is proposed. First, sampling importance resampling filter particle is used to construct the target observation model, which includes selection of particle state, transfer system state, construction of observation model, particle weight update, and resampling process. On this basis, the statistical texture features, moving size features, moving speed, and direction features of the target are extracted, and the target feature framework is constructed by using the target features. The target positioning is estimated based on the similarity features, including describing the target model, representing the candidate model, measuring the specific similarity of the target, and the target positioning process. After the target positioning, the target tracking is realized based on real-time compression. The tracking accuracy of the proposed algorithm is above 90%, the tracking time is kept below 450ns, and the performance of this algorithm is better than that of the target tracking algorithm based on deep learning and fusion features. The proposed algorithm can track the target quickly and accurately, and has strong application advantages.