Large Lump Detection Using a Particle Filter of Hybrid State Variable

Zhijie Wang, Hong Zhang
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引用次数: 3

Abstract

This paper presents a particle filter based solution to the problem of detecting large frozen lumps in an image sequence, taken of the feed to a crusher, which is used for size reduction of oilsand ore. In this application, the objects of interest, i.e., large frozen lumps, are characterized by a high level of image noise, irregular shapes, and uneven and variable surface texture. In addition, more than one large lump can be present in the scene. Our proposed solution integrates evidence of the presence of large lumps over time, by adapting an existing Bayesian framework for joint object detection and tracking. To implement the particle filter, we formulate an application-specific observation model that is required by the Bayesian tracker. Our experimental results show that the proposed solution is capable of detecting multiple large lumps reliably, and that it has the potential of preventing the oilsand crusher from being jammed and leading to improved productivity.
基于混合状态变量粒子滤波的大块检测
本文提出了一种基于粒子滤波的解决方案,用于检测图像序列中的大型冷冻块,该图像序列取自用于减小油砂矿石尺寸的破碎机的饲料。在此应用中,感兴趣的对象,即大型冷冻块,具有高水平的图像噪声,不规则形状以及不均匀和可变的表面纹理。此外,场景中可能出现多个大肿块。我们提出的解决方案通过采用现有的贝叶斯框架进行联合目标检测和跟踪,集成了随着时间推移存在的大块的证据。为了实现粒子滤波,我们制定了贝叶斯跟踪器所需的特定应用的观测模型。实验结果表明,所提出的解决方案能够可靠地检测出多个较大的团块,并具有防止油砂破碎机堵塞和提高生产率的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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