Occlusion Handling via Random Subspace Classifiers for Human Detection
Occlusion Handling via Random Subspace Classifiers for Human Detection
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DOI：
10.1109/tcyb.2013.2255271
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发表时间：
2014-03-01
影响因子：
11.8
通讯作者：
Kuncheva, Ludmila I.
中科院分区：
文献类型：
--
作者：
Marin, Javier;Vazquez, David;Kuncheva, Ludmila I.
本文描述了一种解决静止图像中人体检测的部分遮挡问题的通用方法。选择随机子空间方法(RSM)构建对部分遮挡具有较强鲁棒性的分类器集成。成分分类器是根据它们的单独和组合性能来选择的。这项工作的主要贡献在于我们的方法能够在不影响对非遮挡数据的检测性能的情况下，在存在部分遮挡的情况下提高检测率。与最近的许多方法不同，我们提出了一种方法，该方法不需要手动标记身体部位，不需要定义任何语义空间成分，也不需要使用来自运动或立体的额外数据。此外，该方法还可以很容易地扩展到其他对象类。这些实验是在三个大型数据集上进行的：INRIA人数据集、戴姆勒多人数据集和一个新的具有挑战性的数据集，称为PobleSec，其中相当数量的目标被部分遮挡。对于部分遮挡和非遮挡数据，在分类和检测级别上对不同的方法进行了评估。实验结果表明，我们的检测器在存在部分遮挡的情况下性能优于最先进的方法，同时提供的性能和可靠性与针对非遮挡数据的整体方法相似。我们实验中使用的数据集已公开用于基准目的。
This paper describes a general method to address partial occlusions for human detection in still images. The random subspace method (RSM) is chosen for building a classifier ensemble robust against partial occlusions. The component classifiers are chosen on the basis of their individual and combined performance. The main contribution of this work lies in our approach's capability to improve the detection rate when partial occlusions are present without compromising the detection performance on non occluded data. In contrast to many recent approaches, we propose a method which does not require manual labeling of body parts, defining any semantic spatial components, or using additional data coming from motion or stereo. Moreover, the method can be easily extended to other object classes. The experiments are performed on three large datasets: the INRIA person dataset, the Daimler Multicue dataset, and a new challenging dataset, called PobleSec, in which a considerable number of targets are partially occluded. The different approaches are evaluated at the classification and detection levels for both partially occluded and non-occluded data. The experimental results show that our detector outperforms state-of-the-art approaches in the presence of partial occlusions, while offering performance and reliability similar to those of the holistic approach on non-occluded data. The datasets used in our experiments have been made publicly available for benchmarking purposes.