Trajectory saliency detection using consistency-oriented latent codes from a recurrent auto-encoder

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).

Unsupervised motion saliency map estimation based on optical flow inpainting

The paper addresses the problem of motion saliency in videos, that is, identifying regions that undergo motion departing from its context. We propose a new unsupervised paradigm to compute motion saliency maps. The key ingredient is the flow inpainting stage. Candidate regions are determined from the optical flow boundaries. The residual flow in these regions is given by the difference between the optical flow and the flow inpainted from the surrounding areas. It provides the cue for motion saliency. The method is flexible and general by relying on motion information only. Experimental results on the DAVIS 2016 benchmark demonstrate that the method compares favourably with state-of-the-art video saliency methods.