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TRASMIL: A Local Anomaly Detection Framework Based on Trajectory Segmentation and Multi-instance Learning

机译：TRasmIL：基于轨迹分割和多实例学习的局部异常检测框架

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Wanqi Yang; Yang Gao; Longbing Cao; Wanqi Yanga; Yang Gaoa; Longbing Caob;
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年度 2016
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1. TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning [J] . Wanqi Yang, Yang Gao, Longbing Cao Computer vision and image understanding . 2013,第10期

机译：TRASMIL：基于轨迹分割和多实例学习的局部异常检测框架
2. A hyperspectral anomaly detection framework based on segmentation and convolutional neural network algorithms [J] . Hosseiny Benyamin, Shah-Hosseini Reza International journal of remote sensing . 2020,第17a18期

机译：基于分割和卷积神经网络算法的高光谱异常检测框架
3. Unsupervised learning trajectory anomaly detection algorithm based on deep representation [J] . Zhongqiu Wang, Guan Yuan, Haoran Pei, International Journal of Distributed Sensor Networks . 2020,第12期

机译：基于深度表示的无监督学习轨迹异常检测算法
4. Locality-constrained Multi-Instance Learning for Abnormal Trajectory Detection [C] . Ruoyao Li International Workshop on Materials Engineering and Computer Sciences . 2015

机译：异常轨迹检测的位置约束多实例学习
5. A Cloud Based Automated Anomaly Detection Framework [D] . Kumar, Prathibha Datta 2014

机译：基于云的自动异常检测框架
6. Multi-Channel Generative Framework and Supervised Learning for Anomaly Detection in Surveillance Videos [O] . Tuan-Hung Vu, Jacques Boonaert, Sebastien Ambellouis, 2021

机译：在监控视频中的多通道生成框架和监督学习异常检测
7. Online Detection of Anomalous Sub-trajectories: A Sliding Window Approach Based on Conformal Anomaly Detection and Local Outlier Factor [O] . Laxhammar, Rikard, Falkman, Göran 2012

机译：异常子轨迹的在线检测：基于共形异常检测和局部离群因素的滑动窗口方法

TRASMIL: A Local Anomaly Detection Framework Based on Trajectory Segmentation and Multi-instance Learning

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