Semi-Automatic Cloud-Native Video Annotation for Autonomous Driving

Sánchez-Carballido, Sergio; Senderos, Orti; Nieto, Marcos; Otaegui, Oihana

doi:10.3390/app10124301

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…In CNA, existing studies have primarily concentrated on the CNA principles [15,22], CNA architecture [21], CNA methods [23,24], CNA properties [20], etc. Meanwhile, scholars look at ways to apply cloud-native to modern-day problems, such as healthcare scenarios (e.g., healthcare data acquisition [25], eHealth services orchestration platform [26]), and scientific fields (e.g., cloud-native data repositories [27], autonomous driving [28], e-commerce platform [29], et al) Cloud-native technologies have become the key components of enterprises' digital transformation and migration to the cloud, driven by the advantages of efficiency, security, cost, and developer needs.…”

Section: Cloud-nativementioning

confidence: 99%

Evolutionary Game Analysis on Cloud Providers and Enterprises’ Strategies for Migrating to Cloud-Native under Digital Transformation

Zhang

et al. 2022

Electronics

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Cloud-native is an innovative technology and methodology that is necessary to realize the digital transformation of enterprises. Promoting the wide adoption of cloud-native in cloud providers and enterprises has gained popularity in recent years. According to the technological and commercial characteristics of cloud-native, this paper analyzes the game relationship between cloud providers and enterprises on the selection of cloud-native, and combines evolutionary game theory to establish a model. In addition, empirical analysis indicates the impact of parameter changes on the dynamic evolution process. The results show that (1) enterprises are more vulnerable to the impact of direct benefit to adopt cloud-native, and cloud providers are especially affected by the cost of providing cloud-native; (2) enterprises are more likely to be impacted by the invisible benefit than cloud providers, but the impact has a marginal decreasing effect; (3) the low price is one of the reasons to attract enterprises; (4) enterprises are more concerned about the potential loss caused by the supply and demand mismatch. The results of the discussion provide a reference for all stakeholders to promote the implementation of cloud-native and the digital transformation of enterprises.

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Section: Cloud-nativementioning

confidence: 99%

Evolutionary Game Analysis on Cloud Providers and Enterprises’ Strategies for Migrating to Cloud-Native under Digital Transformation

Zhang

et al. 2022

Electronics

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“…Annotating a large dataset is not an easy task, especially when the projected annotations include multiple dimensions of a computer vision problem, such as in DMS. To annotate the DMD, we applied semi-automatic labelling [ 59 ] methodology aimed to reduce annotation times. The process consists of the generation of a first set of labels based on weak models.…”

Section: Dataset Post-processingmentioning

confidence: 99%

Challenges of Large-Scale Multi-Camera Datasets for Driver Monitoring Systems

Ortega

Cañas

Nieto

et al. 2022

Sensors

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Tremendous advances in advanced driver assistance systems (ADAS) have been possible thanks to the emergence of deep neural networks (DNN) and Big Data (BD) technologies. Huge volumes of data can be managed and consumed as training material to create DNN models which feed functions such as lane keeping systems (LKS), automated emergency braking (AEB), lane change assistance (LCA), etc. In the ADAS/AD domain, these advances are only possible thanks to the creation and publication of large and complex datasets, which can be used by the scientific community to benchmark and leverage research and development activities. In particular, multi-modal datasets have the potential to feed DNN that fuse information from different sensors or input modalities, producing optimised models that exploit modality redundancy, correlation, complementariness and association. Creating such datasets pose a scientific and engineering challenge. The BD dimensions to cover are volume (large datasets), variety (wide range of scenarios and context), veracity (data labels are verified), visualization (data can be interpreted) and value (data is useful). In this paper, we explore the requirements and technical approach to build a multi-sensor, multi-modal dataset for video-based applications in the ADAS/AD domain. The Driver Monitoring Dataset (DMD) was created and partially released to foster research and development on driver monitoring systems (DMS), as it is a particular sub-case which receives less attention than exterior perception. Details on the preparation, construction, post-processing, labelling and publication of the dataset are presented in this paper, along with the announcement of a subsequent release of DMD material publicly available for the community.

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“…Moreover, an automatic textual annotation is generated for each video. [63] proposes an novel tool named Annotation as a Service (AaaS) that is designed to integrate heterogeneous video annotation workflows into containers and take advantage of a cloud service that is highly scalable and reliable based on Kubernetes workloads. The solution has proven to be efficient and resilient and automatic pre-annotations with the proposed strategy reduce the time of human participation in the annotation by up to 80% maximum and 60% on average.…”

Section: Annotation Tools For Video Datamentioning

confidence: 99%

A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data

Zhang,

Jafari,

Nagarkar

2021

Preprint

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Machine learning has been utilized to perform tasks in many different domains such as classification, object detection, image segmentation and natural language analysis. Data labeling has always been one of the most important tasks in machine learning. However, labeling large amounts of data increases the monetary cost in machine learning. As a result, researchers started to focus on reducing data annotation and labeling costs. Transfer learning was designed and widely used as an efficient approach that can reasonably reduce the negative impact of limited data, which in turn, reduces the data preparation cost. Even transferring previous knowledge from a source domain reduces the amount of data needed in a target domain. However, large amounts of annotated data are still demanded to build robust models and improve the prediction accuracy of the model. Therefore, researchers started to pay more attention on auto annotation and labeling. In this survey paper, we provide a review of previous techniques that focuses on optimized data annotation and labeling for video, audio, and text data.

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Semi-Automatic Cloud-Native Video Annotation for Autonomous Driving

Cited by 7 publications

References 25 publications

Evolutionary Game Analysis on Cloud Providers and Enterprises’ Strategies for Migrating to Cloud-Native under Digital Transformation

Evolutionary Game Analysis on Cloud Providers and Enterprises’ Strategies for Migrating to Cloud-Native under Digital Transformation

Challenges of Large-Scale Multi-Camera Datasets for Driver Monitoring Systems

A Survey on Machine Learning Techniques for Auto Labeling of Video, Audio, and Text Data

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