多粒度相关滤波视频跟踪方法

视频跟踪是计算机视觉领域的一个重要研究方向,跟踪算法往往通过融合多种类型的特征来实现较高的性能,但其中多数算法未能充分利用多个特征之间的粒度关系。为此,提出一种基于粒计算思维的多粒度相关滤波视频跟踪算法。对视频图像的特征进行划分,构造出基于各个粒度的相关滤波器并进行独立跟踪,在每帧中根据稳健性评估得分的高低选择最优结果。在此基础上,汇总各帧下的跟踪结果并作为最终输出。在OTB-2013和OTB-20152个公开数据集上进行实验,结果表明,与视频跟踪算法DCFNet相比,该算法在空间鲁棒性与时间鲁棒性上的精准度较高,特别是在快速运动、平面内外旋转和尺度变化的情况下,其具有良好的视频处理能力。

Video Tracking Method Using Multi-granularity Correlation Filters

Video tracking is an important direction in research of computer vision.Many tracking algorithms achieve high performance by integrating multiple types of features,but most of them fail to fully exploit the granularity relationship between multiple features.To address the problem,this paper proposes a multi-granularity video tracking algorithm using multi-granulairty correlation filters based on the concept of granular computing.First,the characteristics of video images are divided and the correlation filters based on different granularities are constructed.Then,the correlation filters implement tracking independently,and select the optimal result based on the score of robustness evaluation in each frame.On this basis,the tracking results of each frame are integrated as the final result.Experiments on two open datasets,OTB-2013 and OTB-2015,show that compared with the video tracking algorithm DCFNet,the proposed algorithm has higher accuracy in space and time robustness.It has excellent video tracking performance especially in the case of fast motion,in/out-of-plane rotation and scale change.