Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification

Choo, Sungkwon; Seo, Wonkyo; Jeong, Dong-ju; Cho, Nam Ik

doi:10.1109/icpr.2018.8545597

Cited by 15 publications

(9 citation statements)

References 17 publications

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“…The existing deep learning CD methods have employed diverse network inputs to train the models. We can categorize them into networks with: single frame [67], [69], [71], [82]- [84], [90], [138]- [140], [143]- [148], 2 frames [66], [68], [70], [85], [87], [89], [92], [136], [149]- [156], 3-10 frames [20], [74], [80], [91], [141], [157], [158], 11-30 frames [81], [86], [137], [142], [159]- [161], and 50 frames [19], [72]. The methods with single frame input primarily rely on the availability of certain labeled frames in a video.…”

Section: B Deep Learning Based Methodsmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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Visual change detection, aiming at segmentation of video frames into foreground and background regions, is one of the elementary tasks in computer vision and video analytics. The applications of change detection include anomaly detection, object tracking, traffic monitoring, human machine interaction, behavior analysis, action recognition, and visual surveillance. Some of the challenges in change detection include background fluctuations, illumination variation, weather changes, intermittent object motion, shadow, fast/slow object motion, camera motion, heterogeneous object shapes and realtime processing. Traditionally, this problem has been solved using hand-crafted features and background modelling techniques. In recent years, deep learning frameworks have been successfully adopted for robust change detection. This article aims to provide an empirical review of the state-of-the-art deep learning methods for change detection. More specifically, we present a detailed analysis of the technical characteristics of different model designs and experimental frameworks. We provide model design based categorization of the existing approaches, including the 2D-CNN, 3D-CNN, ConvLSTM, multi-scale features, residual connections, autoencoders and GAN based methods. Moreover, an empirical analysis of the evaluation settings adopted by the existing deep learning methods is presented. To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods. Finally, we point out the research needs, future directions and draw our own conclusions.

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Section: B Deep Learning Based Methodsmentioning

confidence: 99%

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Mandal¹,

Vipparthi²

2021

Preprint

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“…Several modifications on neural network-based methods, such as deep auto encoding [ 51 ], Gaussian mixture model [ 64 ], convolutional long short-term memory networks [ 65 ], multi-scale convolutional recurrent encoder-decoder [ 66 ] are amongst the latest improvised methods to overcome such issues. Several algorithms have been proposed for this unsupervised anomaly detection but to identify the proper subset for the anomaly detection task is considered difficult.…”

Section: Basic Categorization Of Anomaly Detectionmentioning

confidence: 99%

Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects

Sunny¹,

Patro²,

Karnani³

et al. 2022

Sensors

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Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.

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“…The network takes a red–blue–green (RGB) image in three different scales and generates a foreground segmentation probability mask for the corresponding image. In the period of 2018–2019, numerous deep learning models either based on auto‐encoder [29–31] and CNNs [32–36] have been proposed. However, all these methods are supervised and have been trained on ground truth video frames of datasets and tested on the same types of videos.…”

Section: Related Workmentioning

confidence: 99%

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

Ammar

Bouwmans

Zaghden

et al. 2020

IET Image Processing

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Multi-scale Recurrent Encoder-Decoder Network for Dense Temporal Classification

Cited by 15 publications

References 17 publications

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

An Empirical Review of Deep Learning Frameworks for Change Detection: Model Design, Experimental Frameworks, Challenges and Research Needs

Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects

Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

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