Video benchmarks of human action datasets: a review

Singh, Tej; Vishwakarma, Dinesh Kumar

doi:10.1007/s10462-018-9651-1

Cited by 66 publications

(36 citation statements)

References 161 publications

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“…Surveys on dataset benchmarks for human action recognition from visual data constitute another field of research tackled in [28,44,61,66,98,181]. They aim to guide researchers in the selection of the most suitable dataset for benchmarking their algorithms.…”

Section: Related Surveysmentioning

confidence: 99%

“…Authors in [44] propose a novel dataset, called CONVERSE, that represents complex conversational interactions between two individuals via 3D pose. Similarly, authors in [20,46,180,181,232] present a set of comprehensive reviews of the most commonly used RGB-D video-based activity recognition datasets. Relevant information in each category is extracted in order to help researchers to easily choose appropriate data for their needs.…”

Section: Related Surveysmentioning

confidence: 99%

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Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

327

117

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Human activity recognition (HAR) systems attempt to automatically identify and analyze human activities using acquired information from various types of sensors. Although several extensive review papers have already been published in the general HAR topics, the growing technologies in the field as well as the multi-disciplinary nature of HAR prompt the need for constant updates in the field. In this respect, this paper attempts to review and summarize the progress of HAR systems from the computer vision perspective. Indeed, most computer vision applications such as human computer interaction, virtual reality, security, video surveillance and home monitoring are highly correlated to HAR tasks. This establishes new trend and milestone in the development cycle of HAR systems. Therefore, the current survey aims to provide the reader with an up to date analysis of vision-based HAR related literature and recent progress in the field. At the same time, it will highlight the main challenges and future directions.

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Section: Related Surveysmentioning

confidence: 99%

Section: Related Surveysmentioning

confidence: 99%

Vision-based human activity recognition: a survey

Beddiar

Nini

Sabokrou

et al. 2020

Multimed Tools Appl

327

117

View full text Add to dashboard Cite

show abstract

“…A summary of all the above datasets can be found in Table 6. For a more comprehensive review on human action recognition datasets, the reader is referred to [256].…”

Section: Video Understandingmentioning

confidence: 99%

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

Georgiou

Liu

Chen

et al. 2019

Int J Multimed Info Retr

119

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Higher dimensional data such as video and 3D are the leading edge of multimedia retrieval and computer vision research. In this survey, we give a comprehensive overview and key insights into the state of the art of higher dimensional features from deep learning and also traditional approaches. Current approaches are frequently using 3D information from the sensor or are using 3D in modeling and understanding the 3D world. With the growth of prevalent application areas such as 3D games, self-driving automobiles, health monitoring and sports activity training, a wide variety of new sensors have allowed researchers to develop feature description models beyond 2D. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms. Due to the major importance of the evaluation process, we also present an overview of the current datasets and benchmarks. Moreover, based on more than 330 papers from this study, we present the major challenges and future directions.

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“…YouTube-8M is a dataset of around 8 million videos gathered via the video platform Youtube 3 [1]. The average duration of those videos is 3.75 min in the range of 2 to 8.33 min [26] and for every video metadata according to a predefined vocabulary is given. As other sources describe these metadata as noisy [8] its quality may be assessed as controversial.…”

Section: Related Workmentioning

confidence: 99%

V3C1 Dataset

Berns

Rossetto

Schoeffmann

et al. 2019

Proceedings of the 2019 on International Conference on Multimedia Retrieval

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In this work we analyze content statistics of the V3C1 dataset, which is the first partition of the Vimeo Creative Commons Collection (V3C). The dataset has been designed to represent true web videos in the wild, with good visual quality and diverse content characteristics, and will serve as evaluation basis for the Video Browser Showdown 2019-2021 and TREC Video Retrieval (TRECVID) Ad-Hoc Video Search tasks 2019-2021. The dataset comes with a shot segmentation (around 1 million shots) for which we analyze content specifics and statistics. Our research shows that the content of V3C1 is very diverse, has no predominant characteristics and provides a low selfsimilarity. Thus it is very well suited for video retrieval evaluations as well as for participants of TRECVID AVS or the VBS.

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Video benchmarks of human action datasets: a review

Cited by 66 publications

References 161 publications

Vision-based human activity recognition: a survey

Vision-based human activity recognition: a survey

A survey of traditional and deep learning-based feature descriptors for high dimensional data in computer vision

V3C1 Dataset

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