Department of Computer Science and Engineering, SJB Institute of Technology, Bengaluru, Karnataka 560060, India; Visvesavaraya Technological University, Belgavi, Karnataka 590018, India.
Visvesavaraya Technological University, Belgavi, Karnataka 590018, India; Department of Artificial Intelligence and Machine Learning, SJB Institute of Technology, Bengaluru, Karnataka 560060, India.
Neural Netw. 2024 Feb;170:167-175. doi: 10.1016/j.neunet.2023.11.015. Epub 2023 Nov 7.
Foreground segmentation algorithm aims to precisely separate moving objects from the background in various environments. However, the interference from darkness, dynamic background information, and camera jitter makes it still challenging to build a decent detection network. To solve these issues, a triplet CNN and Transposed Convolutional Neural Network (TCNN) are created by attaching a Features Pooling Module (FPM). TCNN process reduces the amount of multi-scale inputs to the network by fusing features into the Foreground Segmentation Network (FgSegNet) based FPM, which extracts multi-scale features from images and builds a strong feature pooling. Additionally, the up-sampling network is added to the proposed technique, which is used to up-sample the abstract image representation, so that its spatial dimensions match with the input image. The large context and long-range dependencies among pixels are acquired by TCNN and segmentation mask, in multiple scales using triplet CNN, to enhance the foreground segmentation of FgSegNet. The results, clearly show that FgSegNet surpasses other state-of-the-art algorithms on the CDnet2014 datasets, with an average F-Measure of 0.9804, precision of 0.9801, PWC as (0.0461), and recall as (0.9896). Moreover, the FgSegNet with up-sampling achieves the F-measure of 0.9804 which is higher when compared to the FgSegNet without up-sampling.
前景分割算法旨在精确地将运动目标从各种环境的背景中分离出来。然而，由于黑暗、动态背景信息和摄像机抖动的干扰，构建一个像样的检测网络仍然具有挑战性。为了解决这些问题，通过附加特征池化模块 (FPM) 创建了三重卷积神经网络和转置卷积神经网络 (TCNN)。TCNN 处理通过将特征融合到基于 FPM 的前景分割网络 (FgSegNet) 中，从而减少了网络的多尺度输入量，FPM 从图像中提取多尺度特征并构建强大的特征池化。此外，所提出的技术中添加了上采样网络，用于上采样抽象图像表示，使其空间维度与输入图像匹配。TCNN 和分割掩模在多个尺度上使用三重卷积神经网络获取像素之间的大上下文和长程依赖关系，以增强 FgSegNet 的前景分割。结果清楚地表明，FgSegNet 在 CDnet2014 数据集上超越了其他最先进的算法，平均 F-Measure 为 0.9804，精度为 0.9801，PWC 为 (0.0461)，召回率为 (0.9896)。此外，与没有上采样的 FgSegNet 相比，具有上采样的 FgSegNet 的 F-measure 达到了 0.9804。
