18,506 research outputs found
A Causal And-Or Graph Model for Visibility Fluent Reasoning in Tracking Interacting Objects
Tracking humans that are interacting with the other subjects or environment
remains unsolved in visual tracking, because the visibility of the human of
interests in videos is unknown and might vary over time. In particular, it is
still difficult for state-of-the-art human trackers to recover complete human
trajectories in crowded scenes with frequent human interactions. In this work,
we consider the visibility status of a subject as a fluent variable, whose
change is mostly attributed to the subject's interaction with the surrounding,
e.g., crossing behind another object, entering a building, or getting into a
vehicle, etc. We introduce a Causal And-Or Graph (C-AOG) to represent the
causal-effect relations between an object's visibility fluent and its
activities, and develop a probabilistic graph model to jointly reason the
visibility fluent change (e.g., from visible to invisible) and track humans in
videos. We formulate this joint task as an iterative search of a feasible
causal graph structure that enables fast search algorithm, e.g., dynamic
programming method. We apply the proposed method on challenging video sequences
to evaluate its capabilities of estimating visibility fluent changes of
subjects and tracking subjects of interests over time. Results with comparisons
demonstrate that our method outperforms the alternative trackers and can
recover complete trajectories of humans in complicated scenarios with frequent
human interactions.Comment: accepted by CVPR 201
Traffic Danger Recognition With Surveillance Cameras Without Training Data
We propose a traffic danger recognition model that works with arbitrary
traffic surveillance cameras to identify and predict car crashes. There are too
many cameras to monitor manually. Therefore, we developed a model to predict
and identify car crashes from surveillance cameras based on a 3D reconstruction
of the road plane and prediction of trajectories. For normal traffic, it
supports real-time proactive safety checks of speeds and distances between
vehicles to provide insights about possible high-risk areas. We achieve good
prediction and recognition of car crashes without using any labeled training
data of crashes. Experiments on the BrnoCompSpeed dataset show that our model
can accurately monitor the road, with mean errors of 1.80% for distance
measurement, 2.77 km/h for speed measurement, 0.24 m for car position
prediction, and 2.53 km/h for speed prediction.Comment: To be published in proceedings of Advanced Video and Signal-based
Surveillance (AVSS), 2018 15th IEEE International Conference on, pp. 378-383,
IEE
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