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Causality-weighted active learning for abnormal event identification based on the topic model
| Content Provider | Semantic Scholar |
|---|---|
| Author | Fan, Yawen Zheng, Shibao Yang, Hua Zhang, Chongyang Su, Hang |
| Copyright Year | 2012 |
| Abstract | Abnormal event identification in crowded scenes is a fundamental task for video surveillance. However, it is still challenging for most current approaches because of the general insufficiency of labeled data for training, particularly for abnormal data. We propose a novel active-supervised joint topic model for learning activity and training sample collection. First, a multi-class topic model is constructed based on the initial training data. Then the remaining unlabeled data stream is surveyed. The system actively decides whether it can label a new sample by itself or if it has to ask a human annotator. After each query, the current model is incrementally updated. To alleviate class imbalance, causality-weighted method is applied to both likelihood and uncertainty sampling for active learning. Furthermore, a combination of a new measure termed query entropy and the overall classification accuracy is used for assessing the model performance. Experimental results on two real-world traffic videos for abnormal event identification tasks demonstrate the effectiveness of the proposed method. |
| File Format | PDF HTM / HTML |
| DOI | 10.1117/1.OE.51.7.077204 |
| Alternate Webpage(s) | http://ivlab.sjtu.edu.cn/Assets/userfiles/sys_eb538c1c-65ff-4e82-8e6a-a1ef01127fed/files/YawenFan2012_Causality-weighted%20active%20learning%20for%20abnormal%20event%20identification%20based%20on%20the%20topic%20model.pdf |
| Alternate Webpage(s) | https://doi.org/10.1117/1.OE.51.7.077204 |
| Volume Number | 51 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
