Local Context Priors for Object Proposal Generation
Local Context Priors for Object Proposal Generation
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DOI：
10.1007/978-3-642-37331-2_5
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
M. Ristin;Juergen Gall;L. Gool
中科院分区：
文献类型：
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作者：
M. Ristin;Juergen Gall;L. Gool
最先进的目标检测方法大多是基于对不同尺度的图像进行昂贵的穷举搜索。为了减少计算时间，可以执行选择性搜索以获得需要由检测器评估的相关对象假设的小子集。为此，我们利用图像的局部上下文，采用回归来预测可能的物体尺度和位置。此外，我们展示了如何将先验信息（如果可用）集成以改进预测。在Caltech行人和PASCAL VOC数据集上的实验结果表明，我们的方法在计算量更小的情况下达到了穷举搜索方法的检测性能。由于我们对提案的先验分布进行了局部建模，因此它可以很好地泛化，并且可以成功地跨数据集应用。
State-of-the-art methods for object detection are mostly based on an expensive exhaustive search over the image at different scales. In order to reduce the computational time, one can perform a selective search to obtain a small subset of relevant object hypotheses that need to be evaluated by the detector. For that purpose, we employ a regression to predict possible object scales and locations by exploiting the local context of an image. Furthermore, we show how a priori information, if available, can be integrated to improve the prediction. The experimental results on three datasets including the Caltech pedestrian and PASCAL VOC dataset show that our method achieves the detection performance of an exhaustive search approach with much less computational load. Since we model the prior distribution over the proposals locally, it generalizes well and can be successfully applied across datasets.