Extrinsic Behavior Prediction of Pedestrians via Maximum Entropy Markov Model and Graph-Based Features Mining Article Swipe
Yazeed Yasin Ghadi
,
Israr Akhter
,
Hanan Aljuaid
,
Munkhjargal Gochoo
,
Suliman A. Alsuhibany
,
Ahmad Jalal
,
Jeongmin Park
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12125985
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/app12125985
With the change of technology and innovation of the current era, retrieving data and data processing becomes a more challenging task for researchers. In particular, several types of sensors and cameras are used to collect multimedia data from various resources and domains, which have been used in different domains and platforms to analyze things such as educational and communicational setups, emergency services, and surveillance systems. In this paper, we propose a robust method to predict human behavior from indoor and outdoor crowd environments. While taking the crowd-based data as input, some preprocessing steps for noise reduction are performed. Then, human silhouettes are extracted that eventually help in the identification of human beings. After that, crowd analysis and crowd clustering are applied for more accurate and clear predictions. This step is followed by features extraction in which the deep flow, force interaction matrix and force flow features are extracted. Moreover, we applied the graph mining technique for data optimization, while the maximum entropy Markov model is applied for classification and predictions. The evaluation of the proposed system showed 87% of mean accuracy and 13% of error rate for the avenue dataset, while 89.50% of mean accuracy rate and 10.50% of error rate for the University of Minnesota (UMN) dataset. In addition, it showed a 90.50 mean accuracy rate and 9.50% of error rate for the A Day on Campus (ADOC) dataset. Therefore, these results showed a better accuracy rate and low error rate compared to state-of-the-art methods.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app12125985
- https://www.mdpi.com/2076-3417/12/12/5985/pdf?version=1655081530
- OA Status
- gold
- Cited By
- 20
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4282599641
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4282599641Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app12125985Digital Object Identifier
- Title
-
Extrinsic Behavior Prediction of Pedestrians via Maximum Entropy Markov Model and Graph-Based Features MiningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-12Full publication date if available
- Authors
-
Yazeed Yasin Ghadi, Israr Akhter, Hanan Aljuaid, Munkhjargal Gochoo, Suliman A. Alsuhibany, Ahmad Jalal, Jeongmin ParkList of authors in order
- Landing page
-
https://doi.org/10.3390/app12125985Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/12/12/5985/pdf?version=1655081530Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2076-3417/12/12/5985/pdf?version=1655081530Direct OA link when available
- Concepts
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Computer science, Preprocessor, Word error rate, Entropy (arrow of time), Data mining, Graph, Data pre-processing, Hidden Markov model, Artificial intelligence, Markov chain, Pattern recognition (psychology), Machine learning, Theoretical computer science, Quantum mechanics, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
20Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 6, 2023: 6, 2022: 6Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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