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Human Activity Recognition in Video Benchmarks: A Survey

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Advances in Signal Processing and Communication

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 526))

Abstract

Vision-based Human activity recognition is becoming a trendy area of research due to its broad application such as security and surveillance, human–computer interactions, patients monitoring system, and robotics. For the recognition of human activity various approaches have been developed and to test the performance on these video datasets. Hence, the objective of this survey paper is to outline the different video datasets and highlights their merits and demerits under practical considerations. We have categorized these datasets into two part. The first part consists two-dimensional (2D-RGB) datasets and the second part has three-dimensional (3D-RGB) datasets. The most prominent challenges involved in these datasets are occlusions, illumination variation, view variation, annotation, and fusion of modalities. The key specification of these datasets are resolutions, frame rate, actions/actors, background, and application domain. All specifications, challenges involved, and the comparison made in tabular form. We have also presented the state-of-the-art algorithms that give the highest accuracy on these datasets.

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Authors and Affiliations

  1. Department of Information Technology, Delhi Technological University, New Delhi, Delhi, India

    Tej Singh & Dinesh Kumar Vishwakarma

Authors
  1. Tej Singh
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  2. Dinesh Kumar Vishwakarma
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Corresponding author

Correspondence to Tej Singh .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, Reno, NV, USA

    Banmali S. Rawat

  2. Atal Bihari Vajpayee Indian Institute of Information Technology and Management, Gwalior, Gwalior, Madhya Pradesh, India

    Aditya Trivedi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Sanjeev Manhas

  4. Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Vikram Karwal

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Singh, T., Vishwakarma, D.K. (2019). Human Activity Recognition in Video Benchmarks: A Survey. In: Rawat, B., Trivedi, A., Manhas, S., Karwal, V. (eds) Advances in Signal Processing and Communication . Lecture Notes in Electrical Engineering, vol 526. Springer, Singapore. https://doi.org/10.1007/978-981-13-2553-3_24

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  • DOI: https://doi.org/10.1007/978-981-13-2553-3_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2552-6

  • Online ISBN: 978-981-13-2553-3

  • eBook Packages: EngineeringEngineering (R0)

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