Transforming sensor data to the image domain for deep learning — An application to footstep detection

Singh, Monit Shah; Pondenkandath, Vinaychandran; Zhou, Bo; Lukowicz, Paul; Liwickit, Marcus

doi:10.1109/ijcnn.2017.7966182

Cited by 70 publications

(41 citation statements)

References 30 publications

(34 reference statements)

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“…In (Singh et al, 2017), pressure sensor data was transformed to the image via modality transformation. Other similar work include (Ravi et al, 2016;Li et al, 2016b).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,509

927

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Research Highlights (Required)To create your highlights, please type the highlights against each \item command.It should be short collection of bullet points that convey the core findings of the article. It should include 3 to 5 bullet points (maximum 85 characters, including spaces, per bullet point.)• We survey deep learning based HAR in sensor modality, deep model, and application.• We comprehensively discuss the insights of deep learning models for HAR tasks.• We extensively investigate why deep learning can improve the performance of HAR.• We also summarize the public HAR datasets frequently used for research purpose.• We present some grand challenges and feasible solutions for deep learning based HAR. ABSTRACTSensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings. Conventional pattern recognition approaches have made tremendous progress in the past years. However, those methods often heavily rely on heuristic hand-crafted feature extraction, which could hinder their generalization performance. Additionally, existing methods are undermined for unsupervised and incremental learning tasks. Recently, the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas. Since then, deep learning based methods have been widely adopted for the sensor-based activity recognition tasks. This paper surveys the recent advance of deep learning based sensor-based activity recognition. We summarize existing literature from three aspects: sensor modality, deep model, and application. We also present detailed insights on existing work and propose grand challenges for future research.

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“…In (Singh et al, 2017), pressure sensor data was transformed to the image via modality transformation. Other similar work include (Ravi et al, 2016;Li et al, 2016b).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep learning for sensor-based activity recognition: A survey

Wang

Chen

Hao

et al. 2019

Pattern Recognition Letters

1,509

927

View full text Add to dashboard Cite

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“…When it comes to cross-domain transfer learning, its usefulness -especially from natural images such as ImageNet -is subject of an open discussion. There are cases where transfer learning across domains has been proven to be successful [17], [18]. In contrast, there is literature suggesting that this technique is harmful to the final performance of the networks.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Studer

Alberti

Pondenkandath

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of humanannotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pretraining for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval.

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“…The activators of the bottleneck layer are used to produce bottleneck values that are placed in the Softmax classifier. The new Softmax function will map the input image data to obtain the classification results [28].…”

Section: Proposed Deep Convolutional Neural Networkmentioning

confidence: 99%

Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

Chin

Clare

2017

Complexity

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The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if "grooming" is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN) to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google's Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.

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Transforming sensor data to the image domain for deep learning — An application to footstep detection

Cited by 70 publications

References 30 publications

Deep learning for sensor-based activity recognition: A survey

Deep learning for sensor-based activity recognition: A survey

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks

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