Selected Space-Time Based Methods for Action Recognition

Wojciechowski, Sławomir; Kulbacki, Marek; Segen, Jakub; Wyciślok, Rafał; Bąk, Artur; Wereszczyński, Kamil; Wojciechowski, Konrad

doi:10.1007/978-3-662-49390-8_41

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…However, the focus of the table is to provide an overview of some commonly used and effective deep learning methods for HAR rather than to provide an exhaustive list. After conducting broad research with many different spatio-temporal-based HAR methods [184], we focus in this section on the bag of visual words (BoVW) approach to presentation among the most promising and actual classic spatio-temporal methods. The BoVW approach has been widely used and benchmarked for human action recognition and is still relevant today.…”

Section: Spatio-temporal Methodsmentioning

confidence: 99%

Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

Kulbacki

Segen

Chaczko

et al. 2023

Sensors

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The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments.

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Section: Spatio-temporal Methodsmentioning

confidence: 99%

Intelligent Video Analytics for Human Action Recognition: The State of Knowledge

Kulbacki

Segen

Chaczko

et al. 2023

Sensors

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“…The above described problems justify the need for conducting serious research towards elaborating machine—generated benchmarking systems of large variability, resolution and volume. Methodologies for developing benchmarking methodologies for person tracking and action-recognition algorithms must address challenges related to the need for elaborating applications for many different tracking [ 8 ] and action-recognition [ 9 ] environments, including the analysis of surveillance video and urban data understanding. The desired task is to track multiple objects employing simple and compound action recognition and many other aspects of computer vision.…”

Section: Introductionmentioning

confidence: 99%

Application of Crowd Simulations in the Evaluation of Tracking Algorithms

Staniszewski

Foszner

Kostorz

et al. 2020

Sensors

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Tracking and action-recognition algorithms are currently widely used in video surveillance, monitoring urban activities and in many other areas. Their development highly relies on benchmarking scenarios, which enable reliable evaluations/improvements of their efficiencies. Presently, benchmarking methods for tracking and action-recognition algorithms rely on manual annotation of video databases, prone to human errors, limited in size and time-consuming. Here, using gained experiences, an alternative benchmarking solution is presented, which employs methods and tools obtained from the computer-game domain to create simulated video data with automatic annotations. Presented approach highly outperforms existing solutions in the size of the data and variety of annotations possible to create. With proposed system, a potential user can generate a sequence of random images involving different times of day, weather conditions, and scenes for use in tracking evaluation. In the design of the proposed tool, the concept of crowd simulation is used and developed. The system is validated by comparisons to existing methods.

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