IEEE Trans Image Process. 2016 Nov;25(11):5239-51. doi: 10.1109/TIP.2016.2605004. Epub 2016 Aug 31.
Motion detection in video streams is a challenging task for several computer vision applications. Indeed, segmentation of moving and static elements in the scene allows to increase the efficiency of several challenging tasks, such as human-computer interface, robot visions, and intelligent surveillance systems. In this paper, we approach motion detection through a multi-layered artificial neural network, which is able to build for each background pixel a multi-modal color distribution evolving over time through self-organization. According to the winner-take-all rule, each layer of the network models an independent state of the background scene, in response to external disturbing conditions, such as illumination variations, moving backgrounds, and jittering. As a result, our background subtraction method exhibits high generalization capabilities that in combination with a post-processing filtering schema allow to produce accurate motion segmentation. Moreover, we propose an approach to detect anomalous events (such as camera motion) that require background model re-initialization. We describe our method in full details and we compare it against the most recent background subtraction approaches. Experimental results for video sequences from the 2012 and 2014 CVPR Change Detection data sets demonstrate how our methodology outperforms many state-of-the-art methods in terms of detection rate.
视频流中的运动检测对于许多计算机视觉应用来说是一项具有挑战性的任务。实际上，对场景中运动和静态元素的分割可以提高许多具有挑战性任务的效率，例如人机界面、机器人视觉和智能监控系统。在本文中，我们通过一个多层人工神经网络来研究运动检测，该网络能够通过自组织为每个背景像素构建一个随时间演变的多模态颜色分布。根据胜者全拿的规则，网络的每一层都对背景场景的一个独立状态进行建模，以响应外部干扰条件，如光照变化、移动背景和抖动。因此，我们的背景减除方法具有较高的泛化能力，与后处理滤波方案相结合，可以产生准确的运动分割。此外，我们提出了一种检测异常事件（如相机运动）的方法，这些事件需要重新初始化背景模型。我们详细描述了我们的方法，并与最新的背景减除方法进行了比较。来自 2012 年和 2014 年 CVPR 变化检测数据集的视频序列的实验结果表明，在检测率方面，我们的方法优于许多最先进的方法。
