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首页> 外文期刊>Pattern Analysis and Machine Intelligence, IEEE Transactions on >Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model
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Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model

机译：具有通用，自适应，3D车辆模型的车辆监视

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摘要

In automated surveillance, one is often interested in tracking road vehicles, measuring their shape in 3D world space, and determining vehicle classification. To address these tasks simultaneously, an effective approach is the constrained alignment of a prior model of 3D vehicle shape to images. Previous 3D vehicle models are either generic but overly simple or rigid and overly complex. Rigid models represent exactly one vehicle design, so a large collection is needed. A single generic model can deform to a wide variety of shapes, but those shapes have been far too primitive. This paper uses a generic 3D vehicle model that deforms to match a wide variety of passenger vehicles. It is adjustable in complexity between the two extremes. The model is aligned to images by predicting and matching image intensity edges. Novel algorithms are presented for fitting models to multiple still images and simultaneous tracking while estimating shape in video. Experiments compare the proposed model to simple generic models in accuracy and reliability of 3D shape recovery from images and tracking in video. Standard techniques for classification are also used to compare the models. The proposed model outperforms the existing simple models at each task.
机译：在自动监视中，人们经常对跟踪道路车辆，在3D世界空间中测量其形状以及确定车辆类别感兴趣。为了同时解决这些任务，一种有效的方法是将3D车辆形状的现有模型与图像约束对齐。先前的3D车辆模型要么是通用的，要么过于简单，要么僵化而过于复杂。刚性模型仅代表一种车辆设计，因此需要大量的收藏。单个通用模型可以变形为多种形状，但是这些形状太原始了。本文使用可变形的通用3D车辆模型来匹配各种乘用车。在两个极端之间，它的复杂度是可调整的。通过预测和匹配图像强度边缘使模型与图像对齐。提出了新颖的算法，用于将模型拟合到多个静止图像并同时跟踪，同时估计视频中的形状。实验从图像和视频跟踪中将3D形状恢复的准确性和可靠性与提议的模型与简单的通用模型进行了比较。分类的标准技术也用于比较模型。所提出的模型在每个任务上都优于现有的简单模型。

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