Motion recognition, a trivial step employed in several video-based applications, is still a challenging task in real-world complex scenarios containing dynamic noise, varying backgrounds, shadows, improper illuminations, camouflages, etc. Numerous pixel-based change detection techniques employing varied combinations of different feature spaces have been proposed to efficiently overcome many real-world challenges. But ideally, handling all the possible real-world challenges simultaneously is yet to be achieved. Hence, this paper proposes a memory-efficient unique combination of multi-colour feature space with a light-weight intensity-based texture descriptor. The proposed spatially enlarged extended centre-symmetric local binary pattern is combined with YCbCr and RGB colour features for robust pixel representation. The proposed feature space is fed to an extended instance-vote technique for pixel classification. The random and time-subsampled update is employed conditionally for model update, followed by a feedback network that continuously optimizes the local threshold and learning rate parameters of the proposed model. The proposed feature space and model have been evaluated on whole 2014 Change Detection dataset, the largest known dataset. The outperforming performance and memory analysis strengthens its acceptability for real-time applications.

中文翻译：

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使用空间扩展混合特征空间的基于实例投票的运动检测

运动识别是几个基于视频的应用程序中采用的一个微不足道的步骤，在包含动态噪声、变化的背景、阴影、不适当的照明、伪装等的现实世界复杂场景中仍然是一项具有挑战性的任务。已经提出了不同特征空间的组合来有效地克服许多现实世界的挑战。但理想情况下，尚未实现同时处理所有可能的现实世界挑战。因此，本文提出了一种多色特征空间与轻量级基于强度的纹理描述符的内存高效的独特组合。提出的空间放大扩展中心对称局部二进制模式与 YCbCr 和 RGB 颜色特征相结合，以实现稳健的像素表示。提出的特征空间被馈送到用于像素分类的扩展实例投票技术。随机和时间下采样更新有条件地用于模型更新，然后是反馈网络，不断优化所提出模型的局部阈值和学习率参数。所提出的特征空间和模型已经在整个 2014 年变更检测数据集上进行了评估，这是最大的已知数据集。出色的性能和内存分析增强了其对实时应用程序的接受度。所提出的特征空间和模型已经在整个 2014 年变更检测数据集上进行了评估，这是最大的已知数据集。出色的性能和内存分析增强了其对实时应用程序的接受度。所提出的特征空间和模型已经在整个 2014 年变更检测数据集上进行了评估，这是最大的已知数据集。出色的性能和内存分析增强了其对实时应用程序的接受度。