On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts
On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts
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DOI：
10.1109/tcyb.2016.2593940
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发表时间：
2017-11-01
影响因子：
11.8
通讯作者：
Amores, Jaume
中科院分区：
文献类型：
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作者：
Gonzalez, Alejandro;Vazquez, David;Amores, Jaume
尽管最近取得了重大进展，但对象检测在真实的场景中仍然是一个极具挑战性的问题。为了开发在这些条件下成功操作的检测器，利用多个线索、多个成像模态和考虑不同对象视图和姿态的强多视图（MV）分类器变得至关重要。在本文中，我们提供了一个广泛的评估，深入了解这些方面（多通道，多模态和强MV分类器）如何单独和集成在一起时影响准确性。在多模态组件中，我们探索了通过高清光检测和测距获得的RGB和深度图的融合，这是一种开始受到越来越多关注的模态。正如我们的分析所揭示的那样，尽管上述所有方面都显着有助于提高精度，但可见光谱和深度信息的融合可以更大幅度地提高精度。由此产生的检测器不仅在具有挑战性的KITTI基准测试中名列前茅，而且它是建立在非常简单的模块上的，易于实现，计算效率高。
Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI bench-mark, but it is built upon very simple blocks that are easy to implement and computationally efficient.