Moving object segmentation (MOS) in videos received considerable attention because of its broad security-based applications like robotics, outdoor video surveillance, self-driving cars, etc. The current prevailing algorithms highly depend on additional trained modules for other applications or complicated training procedures or neglect the inter-frame spatio-temporal structural dependencies. To address these issues, a simple, robust, and effective unified recurrent edge aggregation approach is proposed for MOS, in which additional trained modules or fine-tuning on a test video frame(s) are not required. Here, a recurrent edge aggregation module (REAM) is proposed to extract effective foreground relevant features capturing spatio-temporal structural dependencies with encoder and respective decoder features connected recurrently from previous frame. These REAM features are then connected to a decoder through skip connections for comprehensive learning named as temporal information propagation . Further, the motion refinement block with multi-scale dense residual is proposed to combine the features from the optical flow encoder stream and the last REAM module for holistic feature learning. Finally, these holistic features and REAM features are given to the decoder block for segmentation. To guide the decoder block, previous frame output with respective scales is utilized. The different configurations of training-testing techniques are examined to evaluate the performance of the proposed method. Specifically, outdoor videos often suffer from constrained visibility due to different environmental conditions and other small particles in the air that scatter the light in the atmosphere. Thus, comprehensive result analysis is conducted on six benchmark video datasets with different surveillance environments. We demonstrate that the proposed method outperforms the state-of-the-art methods for MOS without any pre-trained module, fine-tuning on the test video frame(s) or complicated training.

中文翻译：

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适用于各种监控环境的统一循环视频对象分割框架

视频中的运动对象分割 (MOS) 因其广泛的基于安全的应用而受到广泛关注，如机器人、户外视频监控、自动驾驶汽车等。 当前流行的算法高度依赖于其他应用的额外训练模块或复杂的训练程序或忽略帧间时空结构依赖性。为了解决这些问题，针对 MOS 提出了一种简单、稳健且有效的统一循环边缘聚合方法，其中不需要额外的训练模块或对测试视频帧进行微调。在这里，提出了一个循环边缘聚合模块（REAM）来提取有效的前景相关特征，捕获时空结构依赖性，编码器和相应的解码器特征从前一帧循环连接。时间信息传播。此外，提出了具有多尺度密集残差的运动细化块，以结合来自光流编码器流和最后一个 REAM 模块的特征进行整体特征学习。最后，将这些整体特征和 REAM 特征交给解码器块进行分割。为了引导解码器块，使用具有相应比例的前一帧输出。检查训练测试技术的不同配置以评估所提出方法的性能。具体而言，由于不同的环境条件和空气中的其他小颗粒会散射大气中的光线，户外视频通常会受到可见度的限制。因此，对具有不同监控环境的六个基准视频数据集进行了综合结果分析。