This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

中文翻译：

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基于概率潜在空间模型的视频数据异常检测

本文提出了一种检测视频数据异常的方法。变分自编码器 (VAE) 用于降低视频帧的维数，生成与低维传感数据（例如定位、转向角）相当的潜在空间信息，从而使开发一致的多模态架构成为可能。自动驾驶汽车。采用由离散和连续推理级别定义的自适应马尔可夫跳跃粒子滤波器来预测后续帧并检测新视频序列中的异常。我们的方法在半自动车辆在封闭环境中执行一组任务的不同视频场景中进行了评估。