  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).
