Adaptive Compressive Tracking based on Locality Sensitive Histograms
Adaptive Compressive Tracking based on Locality Sensitive Histograms
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DOI：
10.1016/j.patcog.2017.07.006
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发表时间：
2017-12
期刊：
影响因子：
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通讯作者：
Sixian Chan;Xiaolong Zhou;Junwei Li;Shengyong Chen
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文献类型：
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作者：
Sixian Chan;Xiaolong Zhou;Junwei Li;Shengyong Chen
近年来，作为检测跟踪方法之一的压缩跟踪（CT）方法因其高效性在视频目标跟踪中得到了广泛的研究。然而，它不能很好地处理光照变化，由于其有限的目标表示。为了弥补这一点，我们提出了一种自适应CT算法，它显着改善传统CT在四个方面。首先，在局部敏感直方图的基础上提取有效的光照不变特征来表示目标的外观，这是对光照变化的鲁棒性。其次，采用颜色属性跟踪器对目标位置进行预测，重新构造新的加权判别函数，引入颜色信息，弥补类Haar特征的不足。第三，提出了一种新的模型更新机制，以保持稳定的功能，同时避免在跟踪过程中的噪声外观变化。第四，当可能出现不准确的跟踪时，采用轨迹校正方法来细化跟踪位置。最后，在基准数据集上进行的实验结果表明，我们的跟踪器在综合评价中达到了最先进的性能。
Recently, Compressive Tracking (CT) method, one of tracking-by-detection methods, has been widely explored in video target tracking because of its high efficiency. However, it cannot well deal with illumination variations due to its limited target representation. To remedy this, we propose an adaptive CT algorithm and it significantly improves conventional CT in four aspects. First, the efficient illumination invariant features extracted on the basis of the Locality Sensitive Histograms are used to represent the appearance of a target, which is robust to illumination changes. Second, the color attributes tracker is adopted to predict the target position for re-building the new weighted discriminant function which brings the color information to make up for the inadequacy of Haar-like characteristics. Third, a new model updating mechanism is proposed to preserve the stable features while avoiding the noisy appearance variations during tracking. Fourth, a trajectory rectification method is employed to refine the tracking location when possible inaccurate tracking occurs. Finally, experimental results conducted on benchmark dataset show that our tracker achieves state-of-the-art performance in a comprehensive evaluation.