This paper presents a novel approach of representing dynamic visual scenes with static maps generated from video/image streams. Such representation allows easy visual assessment of motion in dynamic environments. These maps are 2D matrices calculated recursively, in a pixel-wise manner, that is based on the recently introduced concept of Eccentricity data analysis. Eccentricity works as a metric of a discrepancy between a particular pixel of an image and its normality model, calculated in terms of mean and variance of past readings of the same spatial region of the image. While Eccentricity maps carry temporal information about the scene, actual images do not need to be stored nor processed in batches. Rather, all the calculations are done recursively, based on a small amount of statistical information stored in memory, thus resulting in a very computationally efficient (processor- and memory-wise) method. The list of potential applications includes video-based activity recognition, intent recognition, object tracking, video description, and so on.

中文翻译：

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使用偏心率对场景运动进行有效的数据驱动编码

本文提出了一种新颖的方法，用从视频/图像流生成的静态地图来表示动态视觉场景。这种表示允许在动态环境中轻松直观地评估运动。这些映射是基于最近引入的偏心率数据分析概念以像素方式递归计算的2D矩阵。偏心率是图像特定像素与其正态模型之间差异的度量标准，该差异是根据图像同一空间区域的过去读数的均值和方差计算得出的。尽管“偏心率”贴图包含有关场景的时间信息，但实际的图像不需要分批存储或处理。而是根据存储在内存中的少量统计信息来递归地完成所有计算，因此产生了一种非常有效的计算方法（在处理器和内存方面）。潜在的应用程序列表包括基于视频的活动识别，意图识别，对象跟踪，视频描述等。