Generate Anomalies From Normal:A Partial Pseudo Anomaly Augmented Approach For Video Anomaly Detection Article Swipe
Yuanjie Dang
,
Jiangyun Chen
,
Peng Chen
,
Nan Gao
,
Ruohong Huan
,
Dongdong Zhao
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3467595/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3467595/v1
Video Anomaly Detection (VAD) aims to identify unexpected behaviors or objects in videos. Due to the lack of available anomaly samples for training, video anomaly detection is often considered as a one-class classification problem. Specifically, an autoencoder is trained only on normal data, expected to produce large reconstruction errors when detecting anomalies. However, autoencoders can often learn to reconstruct anomalies, leading to detection failures. To address this issue, we introduce a partial appearance based pseudo anomaly generation method in training. Through this approach, the autoencoder becomes more sensitive to the differences between normal and anomalous data, resulting in superior anomaly discrimination capability. We validated our approach on three widely adopted datasets, and experimental results validate the effectiveness of our proposed method. Our source code is published on https://github.com/OctCjy/GenerateAnomaliesFromNormal.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3467595/v1
- https://www.researchsquare.com/article/rs-3467595/latest.pdf
- OA Status
- gold
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387875365
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387875365Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-3467595/v1Digital Object Identifier
- Title
-
Generate Anomalies From Normal:A Partial Pseudo Anomaly Augmented Approach For Video Anomaly DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-23Full publication date if available
- Authors
-
Yuanjie Dang, Jiangyun Chen, Peng Chen, Nan Gao, Ruohong Huan, Dongdong ZhaoList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3467595/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3467595/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-3467595/latest.pdfDirect OA link when available
- Concepts
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Anomaly detection, Autoencoder, Anomaly (physics), Computer science, Artificial intelligence, Pattern recognition (psychology), Code (set theory), Deep learning, Physics, Set (abstract data type), Condensed matter physics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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53Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.we | 69 |
| abstract_inverted_index.Due | 14 |
| abstract_inverted_index.Our | 122 |
| abstract_inverted_index.and | 94, 112 |
| abstract_inverted_index.can | 55 |
| abstract_inverted_index.for | 22 |
| abstract_inverted_index.our | 105, 119 |
| abstract_inverted_index.the | 16, 84, 90, 116 |
| abstract_inverted_index.aims | 5 |
| abstract_inverted_index.code | 124 |
| abstract_inverted_index.lack | 17 |
| abstract_inverted_index.more | 87 |
| abstract_inverted_index.only | 40 |
| abstract_inverted_index.this | 67, 82 |
| abstract_inverted_index.when | 50 |
| abstract_inverted_index.(VAD) | 4 |
| abstract_inverted_index.Video | 1 |
| abstract_inverted_index.based | 74 |
| abstract_inverted_index.data, | 43, 96 |
| abstract_inverted_index.large | 47 |
| abstract_inverted_index.learn | 57 |
| abstract_inverted_index.often | 28, 56 |
| abstract_inverted_index.three | 108 |
| abstract_inverted_index.video | 24 |
| abstract_inverted_index.errors | 49 |
| abstract_inverted_index.issue, | 68 |
| abstract_inverted_index.method | 78 |
| abstract_inverted_index.normal | 42, 93 |
| abstract_inverted_index.pseudo | 75 |
| abstract_inverted_index.source | 123 |
| abstract_inverted_index.widely | 109 |
| abstract_inverted_index.Anomaly | 2 |
| abstract_inverted_index.Through | 81 |
| abstract_inverted_index.address | 66 |
| abstract_inverted_index.adopted | 110 |
| abstract_inverted_index.anomaly | 20, 25, 76, 100 |
| abstract_inverted_index.becomes | 86 |
| abstract_inverted_index.between | 92 |
| abstract_inverted_index.leading | 61 |
| abstract_inverted_index.method. | 121 |
| abstract_inverted_index.objects | 11 |
| abstract_inverted_index.partial | 72 |
| abstract_inverted_index.produce | 46 |
| abstract_inverted_index.results | 114 |
| abstract_inverted_index.samples | 21 |
| abstract_inverted_index.trained | 39 |
| abstract_inverted_index.videos. | 13 |
| abstract_inverted_index.However, | 53 |
| abstract_inverted_index.approach | 106 |
| abstract_inverted_index.expected | 44 |
| abstract_inverted_index.identify | 7 |
| abstract_inverted_index.problem. | 34 |
| abstract_inverted_index.proposed | 120 |
| abstract_inverted_index.superior | 99 |
| abstract_inverted_index.validate | 115 |
| abstract_inverted_index.Detection | 3 |
| abstract_inverted_index.anomalous | 95 |
| abstract_inverted_index.approach, | 83 |
| abstract_inverted_index.available | 19 |
| abstract_inverted_index.behaviors | 9 |
| abstract_inverted_index.datasets, | 111 |
| abstract_inverted_index.detecting | 51 |
| abstract_inverted_index.detection | 26, 63 |
| abstract_inverted_index.failures. | 64 |
| abstract_inverted_index.introduce | 70 |
| abstract_inverted_index.one-class | 32 |
| abstract_inverted_index.published | 126 |
| abstract_inverted_index.resulting | 97 |
| abstract_inverted_index.sensitive | 88 |
| abstract_inverted_index.training, | 23 |
| abstract_inverted_index.training. | 80 |
| abstract_inverted_index.validated | 104 |
| abstract_inverted_index.anomalies, | 60 |
| abstract_inverted_index.anomalies. | 52 |
| abstract_inverted_index.appearance | 73 |
| abstract_inverted_index.considered | 29 |
| abstract_inverted_index.generation | 77 |
| abstract_inverted_index.unexpected | 8 |
| abstract_inverted_index.autoencoder | 37, 85 |
| abstract_inverted_index.capability. | 102 |
| abstract_inverted_index.differences | 91 |
| abstract_inverted_index.reconstruct | 59 |
| abstract_inverted_index.autoencoders | 54 |
| abstract_inverted_index.experimental | 113 |
| abstract_inverted_index.Specifically, | 35 |
| abstract_inverted_index.effectiveness | 117 |
| abstract_inverted_index.classification | 33 |
| abstract_inverted_index.discrimination | 101 |
| abstract_inverted_index.reconstruction | 48 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| abstract_inverted_index.https://github.com/OctCjy/GenerateAnomaliesFromNormal. | 128 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5100659434 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I55712492 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.1611646 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |