Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector Machines Article Swipe
Syed Muhammad Saqlain
,
Tahir Afzal Malik
,
Robina khatoon
,
Syed Saqlain Hassan
,
Faiz Ali Shah
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2019.07948
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2019.07948
Classification of human actions under video surveillance is gaining a lot of attention from computer vision researchers. In this paper, we have presented methodology to recognize human behavior in thin crowd which may be very helpful in surveillance. Research have mostly focused the problem of human detection in thin crowd, overall behavior of the crowd and actions of individuals in video sequences. Vision based Human behavior modeling is a complex task as it involves human detection, tracking, classifying normal and abnormal behavior. The proposed methodology takes input video and applies Gaussian based segmentation technique followed by post processing through presenting hole filling algorithm i.e., fill hole inside objects algorithm. Human detection is performed by presenting human detection algorithm and then geometrical features from human skeleton are extracted using feature extraction algorithm. The classification task is achieved using binary and multi class support vector machines. The proposed technique is validated through accuracy, precision, recall and F-measure metrics.
Related Topics
Concepts
Artificial intelligence
Computer science
Support vector machine
Pattern recognition (psychology)
Segmentation
Computer vision
Task (project management)
Binary classification
Precision and recall
Crowd psychology
Feature (linguistics)
Human skeleton
Feature extraction
Machine learning
Engineering
Systems engineering
Linguistics
Philosophy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2019.07948
- OA Status
- diamond
- Cited By
- 11
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2981281511
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2981281511Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2019.07948Digital Object Identifier
- Title
-
Human Behavior Classification Using Geometrical Features of Skeleton and Support Vector MachinesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Syed Muhammad Saqlain, Tahir Afzal Malik, Robina khatoon, Syed Saqlain Hassan, Faiz Ali ShahList of authors in order
- Landing page
-
https://doi.org/10.32604/cmc.2019.07948Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.32604/cmc.2019.07948Direct OA link when available
- Concepts
-
Artificial intelligence, Computer science, Support vector machine, Pattern recognition (psychology), Segmentation, Computer vision, Task (project management), Binary classification, Precision and recall, Crowd psychology, Feature (linguistics), Human skeleton, Feature extraction, Machine learning, Engineering, Systems engineering, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2023: 1, 2021: 4, 2020: 4Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.validated | 148 |
| abstract_inverted_index.algorithm. | 108, 130 |
| abstract_inverted_index.detection, | 75 |
| abstract_inverted_index.extraction | 129 |
| abstract_inverted_index.precision, | 151 |
| abstract_inverted_index.presenting | 99, 114 |
| abstract_inverted_index.processing | 97 |
| abstract_inverted_index.sequences. | 61 |
| abstract_inverted_index.classifying | 77 |
| abstract_inverted_index.geometrical | 120 |
| abstract_inverted_index.individuals | 58 |
| abstract_inverted_index.methodology | 23, 84 |
| abstract_inverted_index.researchers. | 16 |
| abstract_inverted_index.segmentation | 92 |
| abstract_inverted_index.surveillance | 6 |
| abstract_inverted_index.surveillance. | 37 |
| abstract_inverted_index.Classification | 0 |
| abstract_inverted_index.classification | 132 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.78080779 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |