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首页> 外文会议>Automatic target recognition XXIII >Robust Static and Moving Object Detection System via Multi-scale Attentional Mechanisms
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Robust Static and Moving Object Detection System via Multi-scale Attentional Mechanisms

机译：通过多尺度注意机制的鲁棒静态和动态物体检测系统

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摘要

Real-time detection of objects in video sequences captured from an aerial platforms is a key task for surveillance applications. It is common to perform expensive frame to frame registration as preprocessing to moving object detection in this type of application, and there is no principled approach to the detection of stationary targets. We explore the Spectral Residual algorithm,~6 a fast linearithmic run time saliency model which requires no training and has no temporal dependencies, and is capable of detecting proto-objects in a single image. In this paper we describe methods for enhancing the Spectral Residual saliency algorithm to generate candidate object detections from video sequences captured from moving platforms. These object candidates can then be passed to a classification module for further processing. We describe a method that makes the Spectral Residual algorithm more robust to natural variances in color images, and a pyramidal approach to make the processes more robust to objects of varying size. Furthermore we describe a technique for processing the resulting saliency map into a set of tight bounding boxes suitable for extracting image regions for classification. These methods result in a system that is fast, robust, and efficient with reliable performance for low SWaP surveillance platforms.
机译：从空中平台捕获的视频序列中的对象的实时检测是监视应用程序的关键任务。在这种类型的应用中，通常执行昂贵的帧到帧配准作为对运动对象检测的预处理，并且没有原理性方法来检测静止目标。我们探索了Spectral Residual算法，〜6是一种快速的线性运算运行时显着性模型，该模型不需要训练，也没有时间依赖性，并且能够检测单个图像中的原型对象。在本文中，我们描述了增强频谱残差显着性算法的方法，该算法可从从移动平台捕获的视频序列中生成候选目标检测。然后可以将这些候选对象传递到分类模块以进行进一步处理。我们描述了一种使“光谱残差”算法对彩色图像中的自然方差更鲁棒的方法，以及一种使方法对各种大小的对象更鲁棒的金字塔方法。此外，我们描述了一种技术，用于将所得的显着性地图处理为一组适合提取图像区域进行分类的紧密边界框。这些方法可为低SWaP监视平台提供快速，强大和高效的系统，并具有可靠的性能。

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