Overview of deep-learning based methods for salient object detection in videos

Wang, Qiong; Zhang, Lu; Li, Yan; Kpalma, Kidiyo

doi:10.1016/j.patcog.2020.107340

Cited by 23 publications

(12 citation statements)

References 47 publications

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“…They highlight the 2D CNN, 3D CNN, Clockwork FCN, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Spatiotemporal transformer GRU (STGRU), and GAN methods of deep learning. In [14], Wang et al surveyed salient object detection from video data using deep-learning-based methods. They mentioned the specially designed methods for salient object detection such as fully convolutional network, Spatio-temporal cascade neural network, attentive feedback network, etc.…”

Section: A Literature Surveymentioning

confidence: 99%

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

et al. 2021

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Studies show lots of advanced research on various data types such as image, speech, and text using deep learning techniques, but nowadays, research on video processing is also an emerging field of computer vision. Several surveys are present on video processing using computer vision deep learning techniques, targeting specific functionality such as anomaly detection, crowd analysis, activity monitoring, etc. However, a combined study is still unexplored. This paper aims to present a Systematic Literature Review (SLR) on video processing using deep learning to investigate the applications, functionalities, techniques, datasets, issues, and challenges by formulating the relevant research questions (RQs). This systematic mapping includes 93 research articles from reputed databases published between 2011 and 2020. We categorize the deep learning technique for video processing as CNN, DNN, and RNN based. We observe the significant advancements in video processing between 2017 and 2020, primarily due to the advent of AlexNet, ResNet, and LSTM based deep learning techniques. The prominent fields of video processing research are observed as human action recognition, crowd anomaly detection, and behavior analysis. This SLR is a helpful guide for the researchers to explore the recent literature, available datasets, and existing deep learning techniques for video processing.

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Section: A Literature Surveymentioning

confidence: 99%

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

et al. 2021

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“…In 2020, Qiong WANG et al [4], presented an outline of new advancements in this domain and evaluates the equivalent techniques modern, such as classification of the conventional techniques and their models; review of benchmark datasets as well as usually exploited estimate measures; investigational evaluation of the performances of the conventional technique; recommendations of few shows potential future works for uncertain issues.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For every frame, the saliency map is an output; whereas the value of the pixel shows the equivalent pixel probability belong to a salient object. The superior saliency represents the superior value [4]. Generally, the human notices the SOD which is exploited in many applications like image segmentation, autonomous driving, image change detection, autonomous facial expression recognition, and so forth whereas task is done.…”

Section: Introductionmentioning

confidence: 99%

Improved Deer Hunting Optimization Algorithm for Video- based Salient Object Detection

Puttaswamy¹

2020

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Recent days, many researchers have concentrated on the development in salient object detection which is important in numerous computer vision applications. Nevertheless, the main confront is the competent SOD employing the still images. Hence, this work presents the SOD method employing the developed Improved-Deer Hunting Optimization (Improved-DHO) approach. The approach experiences 3 steps that include saliency mapping, keyframe extraction, contourlet mapping, subsequently, a combination of attained outputs by exploiting the optimal coefficients. Initially, extracted frames are given to saliency as well as contourlet mapping concurrently to decide each pixel quality. Subsequently, the outcomes attained from the contourlet mapping and saliency mapping are combined by exploiting the random coefficients in order to attain the last consequence which is used to recognize the salient objects. Moreover, the developed Improved-DHO is used to select the optimal coefficients in order to detect salient objects. The investigational analysis of developed Improved-DHO based on the performance measures exposes that the developed Improved-DHO obtained a maximum accuracy, specificity, and sensitivity.

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“…Recently, various applications like face recognition, events detection, human tracking, driving assistance, robots control, and video surveillance 1 are blooming in the field of compute vision. [2][3][4][5][6] Particularly, video surveillance is a crucial task of these days to protect the important infrastructures and also to enhance the public safety as well. 2,[7][8][9][10][11] With the rapid expansion in the field of technology, the advanced techniques have brought out the cost of the high-quality cameras.…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4][5][6] Particularly, video surveillance is a crucial task of these days to protect the important infrastructures and also to enhance the public safety as well. 2,[7][8][9][10][11] With the rapid expansion in the field of technology, the advanced techniques have brought out the cost of the high-quality cameras. This makes the video surveillance systems to be more popular and inexpensive.…”

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confidence: 99%

Convolutional neural network based object detection system for video surveillance application

Bhimavarapu

Ramaraju

Nagajyothi³

et al. 2022

Concurrency and Computation

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Summary Video surveillance is emerging as a promising solution for the humans to lead a peaceful and independent life in their homes. The recognition and localization of moving objects plays a central role in the video surveillance. The manual surveillance is time consuming and tedious. Therefore, novel object detection via optimized deep learning model is developed in this work that supports the video surveillance application. In the initial phase, proposed angle and distance based Local Binary Pattern (LBP) features are extracted. Subsequently, these extracted features are subjected to object detection phase, where optimized Convolutional Neural Network (CNN) will expose the information about the detected object. Further, the learning quality of CNN is decided by the weight parameter, which is responsible to distinguish the objects with high accuracy. Therefore, a hybrid optimization concept referred as Sealion Leader Update with Particles (SLUP) is introduced in this research work to fine‐tune the weight of CNN. Finally, a comparative analysis is made between the proposed and the extant approaches in terms of “positive, negative, and other measures.”

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Overview of deep-learning based methods for salient object detection in videos

Cited by 23 publications

References 47 publications

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

Video Processing Using Deep Learning Techniques: A Systematic Literature Review

Improved Deer Hunting Optimization Algorithm for Video- based Salient Object Detection

Convolutional neural network based object detection system for video surveillance application

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