Interactive concepts for shaping generative models of spatial behavior

Ronny Hug, W. Hübner, Michael Arens
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Abstract

A technique widely used in video based situation assessment, and especially in anomaly detection, is the analysis of spatial behavior in terms of motion profiles recorded along trajectories. An intuitive assessment metric is the deviation from normal behavior, where generative models are a natural choice for capturing the underlying statistics. Applying such outlier methods in open world scenarios has the drawback that long observation times are required, in order to fully determine the model, while underdetermined models are very prone to generate non-intuitive or wrong results. In order to address this problem, the usage of interactive concepts for supporting the learning process and refining learned models is proposed. Thereby, the method keeps track of automatically integrated observations and stochastic priors generated by examples provided by the user. Examples can be given in terms of individual labeled samples, or in terms of complex pdfs. The feasibility of the proposed approach is illustrated on the BIWI Walking Pedestrians dataset, using partitioned Gaussian mixture models as the generative model.
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互动概念塑造空间行为的生成模型
一种广泛应用于基于视频的态势评估,特别是异常检测的技术,是根据沿轨迹记录的运动剖面来分析空间行为。一个直观的评估指标是对正常行为的偏离,其中生成模型是捕获底层统计数据的自然选择。在开放世界场景中应用这种离群值方法的缺点是需要很长的观测时间才能完全确定模型,而未确定的模型很容易产生不直观或错误的结果。为了解决这个问题,提出了使用交互式概念来支持学习过程和改进学习模型。因此,该方法跟踪由用户提供的示例生成的自动集成观测值和随机先验。示例可以以单个标记样本的形式给出,也可以以复杂的pdf格式给出。在BIWI步行行人数据集上,使用分割的高斯混合模型作为生成模型,说明了该方法的可行性。
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