5,134 research outputs found
A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects
Recently, Minimum Cost Multicut Formulations have been proposed and proven to
be successful in both motion trajectory segmentation and multi-target tracking
scenarios. Both tasks benefit from decomposing a graphical model into an
optimal number of connected components based on attractive and repulsive
pairwise terms. The two tasks are formulated on different levels of granularity
and, accordingly, leverage mostly local information for motion segmentation and
mostly high-level information for multi-target tracking. In this paper we argue
that point trajectories and their local relationships can contribute to the
high-level task of multi-target tracking and also argue that high-level cues
from object detection and tracking are helpful to solve motion segmentation. We
propose a joint graphical model for point trajectories and object detections
whose Multicuts are solutions to motion segmentation {\it and} multi-target
tracking problems at once. Results on the FBMS59 motion segmentation benchmark
as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark
demonstrate the promise of this joint approach
Event-Based Motion Segmentation by Motion Compensation
In contrast to traditional cameras, whose pixels have a common exposure time,
event-based cameras are novel bio-inspired sensors whose pixels work
independently and asynchronously output intensity changes (called "events"),
with microsecond resolution. Since events are caused by the apparent motion of
objects, event-based cameras sample visual information based on the scene
dynamics and are, therefore, a more natural fit than traditional cameras to
acquire motion, especially at high speeds, where traditional cameras suffer
from motion blur. However, distinguishing between events caused by different
moving objects and by the camera's ego-motion is a challenging task. We present
the first per-event segmentation method for splitting a scene into
independently moving objects. Our method jointly estimates the event-object
associations (i.e., segmentation) and the motion parameters of the objects (or
the background) by maximization of an objective function, which builds upon
recent results on event-based motion-compensation. We provide a thorough
evaluation of our method on a public dataset, outperforming the
state-of-the-art by as much as 10%. We also show the first quantitative
evaluation of a segmentation algorithm for event cameras, yielding around 90%
accuracy at 4 pixels relative displacement.Comment: When viewed in Acrobat Reader, several of the figures animate. Video:
https://youtu.be/0q6ap_OSBA
Robust pedestrian detection and tracking in crowded scenes
In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases
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