Track and Cut: simultaneous tracking and segmentation of multiple objects with graph cuts
Résumé
This paper presents a new method to both track and segment multiple objects in videos using min-cut/max-flow optimizations. We introduce objective functions that combine low-level pixel wise measures (color, motion), high-level observations obtained via an independent detection module, motion prediction, and contrast-sensitive contextual regularization. One novelty is that external observations are used without adding any association step. The observations are image regions (pixel sets) that can be provided by any kind of detector. The minimization of appropriate cost functions simultaneously allows “detection-before-track” tracking (track-to-observation assignment and automatic initialization of new tracks) and segmentation of tracked objects. When several tracked objects get mixed up by the detection module (e.g., a single foreground detection mask is obtained for several objects close to each other), a second stage of minimization allows the proper tracking and segmentation of these individual entities despite the confusion of the external detection module.