论文标题
基于概率潜在空间模型的视频数据中的异常检测
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models
论文作者
论文摘要
本文提出了一种检测视频数据中异常情况的方法。差异自动编码器（VAE）用于降低视频框架的维度，生成可与低维感觉数据相媲美的潜在空间信息（例如，定位，转向角度），使可行的自动驾驶汽车的一致多模式结构的开发。由离散和连续推理水平定义的适应的马尔可夫跳跃粒子滤波器用于预测以下帧并检测新视频序列中的异常。我们的方法在不同的视频场景上进行了评估，其中半自治车在封闭的环境中执行一组任务。
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.
