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首页> 外文会议>Anniversary international conference on multimedia modeling >MOSRO: Enabling Mobile Sensing for Real-Scene Objects with Grid Based Structured Output Learning
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MOSRO: Enabling Mobile Sensing for Real-Scene Objects with Grid Based Structured Output Learning

机译：MOSRO：通过基于网格的结构化输出学习为实景对象启用移动感测

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摘要

Visual objects in mobile photos are usually captured in uncontrolled conditions, such as various viewpoints, positions, scales, and background clutter. In this paper, therefore, we developed a MObile Sensing framework for robust Real-scene Object recognition and localization (MOSRO). By extending the conventional structured output learning with the proposed grid based representation as the output structure, MOSRO is not only able to locate the visual objects precisely but also achieve real-time performances. The experimental results showed that the proposed framework outperforms the state-of-the-art methods on public real-scene image datasets. Further, to demonstrate its effectiveness for practical applications, the proposed MOSRO framework was implemented on Android mobile platforms as a prototype system for sensing various business signs on the street and instantly retrieving relevant information of the recognized businesses.
机译：移动照片中的视觉对象通常是在不受控制的条件下捕获的，例如各种视点，位置，比例和背景混乱。因此，在本文中，我们开发了用于健壮的实景对象识别和定位（MOSRO）的MObile传感框架。通过使用建议的基于网格的表示作为输出结构扩展常规的结构化输出学习，MOSRO不仅可以精确定位视觉对象，而且还可以实现实时性能。实验结果表明，所提出的框架优于公开的真实场景图像数据集上的最新方法。此外，为了证明其在实际应用中的有效性，建议的MOSRO框架已在Android移动平台上实现，作为一种原型系统，用于感知街道上的各种商业标志并立即检索已识别商业的相关信息。

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