An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

Abstract

In many instances, outdoor surveillance systems suffer from atrocious weather conditions such as rain, since images or videos captured by such vision systems in rainy days may undergo severe visual dilapidations. This can cause a glitch in those algorithms which are further used for object detection and tracking. Therefore, an ancillary video processing algorithm namely, video deraining is necessary prior to the implementation of object detection and tracking. This indicates the requirement of a time-consuming and complicated two-step process for the detection of moving objects in a rainy environment. This paper proposes an automated single-stage formulation for the conventional three-stage procedure such as rain steak removal, original data recovery and moving object detection as simultaneous operation in the tensor framework. The brilliance of this work is confined in the efficient formulation of an operator termed as Slice Rotational Total Variation (SRTV), to unify the different rain patterns into a common pattern so that any form of rain pattern can be effectively removed from the rainy data in a visually appealing manner by preserving the important details of the data. In this paper, SRTV regularization and tensor low-rank minimization are utilized for the effective deraining as well as efficient retrieval of clean background. Besides, l 1 norm and Tensor Total Variation (TTV) regularizers together with SRTV regularizer are employed for the faithful detection of derained moving objects. The experimental results show that the proposed method outperforms the state of the art methods in terms of deraining capability and accurate moving object detection.

Deep ensemble model-based moving object detection and classification using SAR images

“…Recently, SAR images have been detected with the aid of deep learning algorithms such as convolutional neural networks (CNNs) and long short-term memory (LSTM) (Yu et al, 2020). Advanced objective models have been used as traditional approaches, in which the sums of Gaussians are insufficient for optimization-based thresholding methods applied to SAR imagery (Baiju and George, 2020). Image classification techniques benefit from CNN because it can learn highly abstract characteristics and operate with fewer parameters.…”