摘要
TLD(Tracking-Learning-Detection)算法是一种广泛应用的车辆跟踪算法,但其在低分辨率视频下易出现跟踪漂移或者丢失等问题,为此,对传统的TLD算法进行改进。针对TLD算法中金字塔光流跟踪器易受光照影响,出现跟踪失败问题,采用具有较强跟踪性能的CT跟踪器,并研究跟踪失败自检测策略,以提高算法的跟踪性能。此外,通过对2bit BP-HOG特征(形状和纹理特征)描述算子进行多特征融合,有效克服了低分辨率环境下纹理特征提取不准确造成检测器准确度低的问题。实验表明,改进算法在鲁棒性和跟踪速率方面都有所提高。
TLD （Tracking-Learning-Detection） is a widely adopted algorithm in the research topics of vehicle tracking. However, the traditional TLD is easily to lose targets and locate the wrong targets in a low-resolution video, and that is why the traditional TLD algorithm should be improved. As the pyramid optical flow tracker in traditional TLD fails to work well in poor illumination scenes, a novel vehicle tracking algorithm by applying the CT（ Compressive Tracking） into the traditional TLD algorithm is proposed to obtain the vehicle location fast when the targets are lost. Besides,with the multi-features fusion of the 2bitBP-HOG feature descriptor, which is composed of shape and texture information in tracking phase, the improved algorithm obtains higher-performance tracking results than those of traditional TLD in low-resolution condition, not only tracking precision but also time complexity. Experiment results validate the improvement of the proposed algorithm, both on robustness and tracking rates.
出处
《计算机应用与软件》
CSCD
2016年第12期264-269,共6页
Computer Applications and Software
基金
广西高校图像图形智能处理重点实验室立项课题(GIIP201403)
广西信息科学实验中心2014年度一般基金项目
广西高校云计算与复杂系统重点实验室立项课题(15210)
广西科技计划项目(桂科攻1598018-6)
