论文标题
使用重复的自动编码器的轨迹显着性检测使用一致性的潜在代码
Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder
论文作者
论文摘要
在本文中，我们关注的是从视频序列中检测到进行性动态显着性。更确切地说，我们对与运动有关的显着性感兴趣，并且可能会随着时间的流逝而逐步出现。它可能与触发警报，专门处理或检测特定事件有关。轨迹代表了支持渐进动态显着性检测的最佳方法。因此，我们将讨论轨迹显着性。如果轨迹偏离与给定上下文相关的常见运动模式的正常轨迹，则轨迹将被视为显着。首先，我们需要一个紧凑的轨迹歧视性表示。我们采用（几乎）基于学习的方法。由复发自动编码器估计的潜在代码提供了所需的表示形式。此外，我们通过自动编码器损耗函数对正常（相似）轨迹的一致性。轨迹代码到原型代码的距离占正态性的手段是检测显着轨迹的手段。我们验证了关于合成和实际轨迹数据集的轨迹显着性检测方法，并突出显示其不同组件的贡献。我们表明，我们的方法在从火车站中获得的行人轨迹数据集中得出的几种方案上优于现有方法（Alahi 2014）。
In this paper, we are concerned with the detection of progressive dynamic saliency from video sequences. More precisely, we are interested in saliency related to motion and likely to appear progressively over time. It can be relevant to trigger alarms, to dedicate additional processing or to detect specific events. Trajectories represent the best way to support progressive dynamic saliency detection. Accordingly, we will talk about trajectory saliency. A trajectory will be qualified as salient if it deviates from normal trajectories that share a common motion pattern related to a given context. First, we need a compact while discriminative representation of trajectories. We adopt a (nearly) unsupervised learning-based approach. The latent code estimated by a recurrent auto-encoder provides the desired representation. In addition, we enforce consistency for normal (similar) trajectories through the auto-encoder loss function. The distance of the trajectory code to a prototype code accounting for normality is the means to detect salient trajectories. We validate our trajectory saliency detection method on synthetic and real trajectory datasets, and highlight the contributions of its different components. We show that our method outperforms existing methods on several scenarios drawn from the publicly available dataset of pedestrian trajectories acquired in a railway station (Alahi 2014).
