For sequential data, a change point is a moment of abrupt regime switch in data streams. Such changes appear in different scenarios, including simpler data from sensors and more challenging video surveillance data. We need to detect disorders as fast as possible. Classic approaches for change point detection (CPD) might underperform for semi-structured sequential data because they cannot process its structure without a proper representation. We propose a principled loss function that balances change detection delay and time to a false alarm. It approximates classic rigorous solutions but is differentiable and allows representation learning for deep models. We consider synthetic sequences, real-world data sensors and videos with change points. We carefully labelled available data with change point moments for video data and released it for the first time. Experiments suggest that complex data require meaningful representations tailored for the specificity of the CPD task -- and our approach provides them outperforming considered baselines. For example, for explosion detection in video, the F1 score for our method is $0.53$ compared to baseline scores of $0.31$ and $0.35$.
翻译:对于连续数据来说,一个变化点是数据流突变的系统切换时刻。这种变化出现在不同的情景中,包括传感器的更简单数据和更具挑战性的视频监视数据。我们需要尽快地检测疾病。典型的改变点探测方法(CPD)对于半结构有序数据来说可能表现不佳,因为它们无法在没有适当代表的情况下处理其结构。我们提出了一个原则性损失函数,将探测延迟和时间与假警报相平衡。它大致上是典型的严格解决方案,但可以区别,并允许深层模型的描述性学习。我们考虑合成序列、真实世界数据传感器和带变化点的视频。我们仔细标记了可用数据,为视频数据设定了改变点,并首次公布了这些数据。实验表明,复杂的数据需要根据CPD任务的特殊性进行有意义的描述,而我们的方法则提供了超出所考虑的基线。例如,对于视频中的爆炸探测,我们方法的F1得分为0.53美元,而基线得分为0.31美元和0.35美元。