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A Recursive Bayesian Filter for Anomalous Behavior Detection in Trajectory Dataial anomalous driving behaviors dynamically over time. The proposed filter performs unsupervised detection in single trajectory with solely the local features. No training process is required to characterize the anomalous behaviors. Based on the results of individual trajectories collective behavior
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2522-0454 e the accuracy and robustness of the system. And the book helps readers to solve these problems. The intelligent information processing technology involved is equipped with simulation verification, which can be978-981-33-4518-8978-981-33-4516-4Series ISSN 2522-0454 Series E-ISSN 2522-0462