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Multi-view Vehicle Detection based on Part Model with Active Learning

机译：基于零件模型的主动学习多视图车辆检测

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摘要

Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods.
机译：如今，大多数车辆检测方法旨在仅检测单视图车辆，并且性能容易受到部分遮挡的影响。因此，提出了一种新颖的多视点车辆检测系统来解决部分遮挡的问题。提出的系统分为两个步骤：背景过滤和零件模型。背景过滤步骤用于过滤掉树木，天空和其他道路背景对象。在零件模型步骤中，通过使用建议的主动学习算法收集的样本对每个零件模型进行训练。本文验证了背景滤波方法和零件模型算法在多视点汽车检测中的性能。所提出的方法的性能优于先前提出的方法。

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