Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination
Learning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samp...
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| Format: | Article |
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/1099-4300/27/7/655 |
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| author | Chunxiang Niu Siyu Meng Rong Wang |
| author_facet | Chunxiang Niu Siyu Meng Rong Wang |
| author_sort | Chunxiang Niu |
| collection | DOAJ |
| description | Learning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samples, leading to misjudgments and high false alarm rates. To address these challenges, we propose a dual triplet contrastive loss strategy. This approach employs dual memory units to extract four key feature categories: hard samples, negative samples, positive samples, and anchor samples. Contrastive loss is utilized to constrain the distance between hard samples and other samples, enabling accurate identification of hard samples and enhancing the discriminative ability of hard samples and abnormal features. Additionally, a multi-scale feature perception module is designed to capture feature information at different levels, while an adaptive global–local feature fusion module constructs complementary feature enhancement through feature fusion. Experimental results demonstrate the effectiveness of our method, achieving AUC scores of 87.16% on the UCF-Crime dataset and AP scores of 83.47% on the XD-Violence dataset. |
| format | Article |
| id | doaj-art-3d5fff80721842e8bca9784c576a86e4 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-3d5fff80721842e8bca9784c576a86e42025-08-20T03:58:27ZengMDPI AGEntropy1099-43002025-06-0127765510.3390/e27070655Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample DiscriminationChunxiang Niu0Siyu Meng1Rong Wang2College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaCollege of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaCollege of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, ChinaLearning discriminative features between abnormal and normal instances is crucial for video anomaly detection within the multiple instance learning framework. Existing methods primarily focus on instances with the highest anomaly scores, neglecting the identification and differentiation of hard samples, leading to misjudgments and high false alarm rates. To address these challenges, we propose a dual triplet contrastive loss strategy. This approach employs dual memory units to extract four key feature categories: hard samples, negative samples, positive samples, and anchor samples. Contrastive loss is utilized to constrain the distance between hard samples and other samples, enabling accurate identification of hard samples and enhancing the discriminative ability of hard samples and abnormal features. Additionally, a multi-scale feature perception module is designed to capture feature information at different levels, while an adaptive global–local feature fusion module constructs complementary feature enhancement through feature fusion. Experimental results demonstrate the effectiveness of our method, achieving AUC scores of 87.16% on the UCF-Crime dataset and AP scores of 83.47% on the XD-Violence dataset.https://www.mdpi.com/1099-4300/27/7/655abnormal behavior detectionmultiple instance learninghard instancecontrastive loss functionmulti-scale feature |
| spellingShingle | Chunxiang Niu Siyu Meng Rong Wang Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination Entropy abnormal behavior detection multiple instance learning hard instance contrastive loss function multi-scale feature |
| title | Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination |
| title_full | Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination |
| title_fullStr | Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination |
| title_full_unstemmed | Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination |
| title_short | Enhanced Video Anomaly Detection Through Dual Triplet Contrastive Loss for Hard Sample Discrimination |
| title_sort | enhanced video anomaly detection through dual triplet contrastive loss for hard sample discrimination |
| topic | abnormal behavior detection multiple instance learning hard instance contrastive loss function multi-scale feature |
| url | https://www.mdpi.com/1099-4300/27/7/655 |
| work_keys_str_mv | AT chunxiangniu enhancedvideoanomalydetectionthroughdualtripletcontrastivelossforhardsamplediscrimination AT siyumeng enhancedvideoanomalydetectionthroughdualtripletcontrastivelossforhardsamplediscrimination AT rongwang enhancedvideoanomalydetectionthroughdualtripletcontrastivelossforhardsamplediscrimination |