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Performance Improvement of Multi-class Detection Using Greedy Algorithm for Viola-Jones Cascade Selection

机译：Viola-Jones级联选择的贪婪算法提高多类检测性能

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摘要

This paper aims to study the problem of multi-class object detection in video stream with Viola-Jones cascades. An adaptive algorithm for selecting Viola-Jones cascade based on greedy choice strategy in solution of the N-armed bandit problem is proposed. The efficiency of the algorithm on the problem of detection and recognition of the bank card logos in the video stream is shown. The proposed algorithm can be effectively used in documents localization and identification, recognition of road scene elements, localization and tracking of the lengthy objects , and for solving other problems of rigid object detection in a heterogeneous data flows. The computational efficiency of the algorithm makes it possible to use it both on personal computers and on mobile devices based on processors with low power consumption.
机译：本文旨在研究具有Viola-Jones级联的视频流中的多类目标检测问题。提出了一种基于贪婪选择策略的中提琴-琼斯级联选择算法，以解决N武装匪徒问题。示出了算法在视频流中的银行卡徽标的检测和识别问题上的效率。所提出的算法可以有效地用于文档的定位和识别，道路场景元素的识别，冗长对象的定位和跟踪，以及解决异构数据流中刚性对象检测的其他问题。该算法的计算效率使其可以在基于低功耗处理器的个人计算机和移动设备上使用。

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