Authors:
Aurélie Bugeau
and
Patrick Pérez
Affiliation:
INRIA, Centre Rennes - Bretagne Atlantique, France
Keyword(s):
Tracking, Graph Cuts.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
;
Tracking of People and Surveillance
Abstract:
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 (connected components of foreground detection masks in the experiments), motion prediction and contrast-sensitive contextual regularization. One novelty is that external observations are used without adding any association step. The minimization of these 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., single foreground detection mask for objects close to each other), a second stage of minimization allows the proper tracking and segmentation of these individual entities despite the observation co
nfusion. Experiments on sequences from PETS 2006 corpus demonstrate the ability of the method to detect, track and precisely segment persons as they enter and traverse the field of view, even in cases of occlusions (partial or total), temporary grouping and frame dropping.
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