计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 181-186.doi: 10.11896/JsJkx.190500093
程中建, 周双娥, 李康
CHENG Zhong-Jian, ZHOU Shuang-e and LI Kang
摘要: 目标跟踪是计算机视觉中的一个重要研究领域,在交通导航、自动驾驶、机器人技术等众多方面有着广泛应用。基于局部稀疏表示的生成式模型算法ASLA的速度快、跟踪准确性高,但是在复杂跟踪环境下,例如目标局部遮挡、目标外观剧烈变化等,往往会丢失目标。文中分析原算法跟踪原理得到了产生目标跟踪丢失的原因。基于ASLA算法,提出了3点改进方法:1)适应跟踪目标区域大小,采用多尺度分块方式,获取互补的目标局部信息;2)在ASLA特征池化过程中根据分块重构误差建模分块自适应权重,以区分不同分块中包含的判别信息,且在多尺度池化特征中引入不同尺度下的目标遮挡信息作为权重;3)在模板更新时,利用最近帧跟踪结果的稀疏表示权重,使更新模板更相似最近跟踪结果,提高了算法的鲁棒性。实验结果表明,该算法在复杂跟踪环境下相比ASLA等具有更高的跟踪准确度,能够实时、准确地跟踪到目标。
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