摘要
目标表观变化的处理是视觉跟踪领域极具挑战性的问题,该文针对这一问题,在粒子滤波框架下提出一种高效的基于超像素的L1跟踪方法(SuperPixel-L1 tracker,SPL1)。首先利用具有结构性信息的中层视觉线索(超像素)构造字典来对目标的表观建模;然后求解由粒子表示的候选目标状态的L1范数最小化,把重构误差最小的候选状态作为跟踪的结果;最后进一步改进了字典的在线更新策略,不论目标发生遮挡与否,字典都被学习更新;为了降低目标发生漂移的可能,更新时保留初始帧的信息。仿真结果验证了SPL1在目标发生长时间遮挡、尺度和光照变化时依然能够稳定地跟踪目标。
Handling appearance variations is a very challenging issue for visual tracking. In this paper, an effective superpixel based L1 tracking method （SuperPixel-L1 tracker, SPL1） is proposed to deal with the above problem in a particle filter framework. First, the mid-level visual cue with structural information is exploited to construct the dictionary and model the object appearance. Then each candidate state defined by a particle is solved via L1 minimization. The candidate with the smallest reconstruction error is selected as the tracking result. Finally, the online dictionary updating strategy is further improved. The dictionary needs to be updated regardless of whether the object is occluded or not. The initial frame information is retained during the updating process to reduce the possibility of the object drift. Simulation results show that SPL1 tracker can still stably track the object under the circumstance of long-term occlusion, large scale and illumination changes.
出处
《电子与信息学报》
EI
CSCD
北大核心
2014年第10期2393-2399,共7页
Journal of Electronics & Information Technology
基金
国家自然科学基金(60971098
61201345)
现代信息科学与网络技术北京市重点实验室开放课题(XDXX1308)资助课题
关键词
视觉跟踪
在线学习
表观变化
稀疏表示
超像素
Visual tracking
Online learning
Appearance changes
Sparse representation
Superpixel
