COST: An Approach for Camera Selection and Multi-Object Inference Ordering in Dynamic Scenes
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Citations
Robust Tracking in A Camera Network: A Multi-Objective Optimization Framework
Can You See Me Now? Sensor Positioning for Automated and Persistent Surveillance
Recent Advances in Camera Planning for Large Area Surveillance: A Comprehensive Review
Content and task-based view selection from multiple video streams
Camera selection for tracking in distributed smart camera networks
References
A Quantitative Evaluation of Video-based 3D Person Tracking
A Survey of Algorithms for Real-Time Bayesian Network Inference
Distributed Occlusion Reasoning for Tracking with Nonparametric Belief Propagation
Reduction of computational complexity in Bayesian networksthrough removal of weak dependences
Approximating probabilistic inference in Bayesian belief networks
Related Papers (5)
M 2 Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene
Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements
Frequently Asked Questions (9)
Q2. How is the algorithm used to estimate people's positions?
The algorithm cycles between using segmentation to estimate people’s ground plane positions and using ground plane position estimates to obtain segmentations; the process is iterated until stable.
Q3. How many occluded voxels can be added to a camera?
The number of occluded voxels that can be added due to dependencies depends on the selection of dependencies and the accuracy of the position estimate of the occluder.
Q4. What is the problem of a naive approach?
A naive approach (by considering all pairwise interactions of all parts of all people) would involve constructing a large Bayesian network with loops; however, this results in an intractable optimization problem.
Q5. What is the probability of a person’s visibility in a camera?
Let dV be a differential volume element(voxel) which might be included in part j of person i. The Occluder Region, Ωk(dV ), of a differential element dV in camera k is defined as the 3D region in which another person, l, must be present so that dV would not be visible in camera k (See Fig 3).
Q6. What is the probability of a person being seen in the confuser space?
The weight cl,m is proportional to the probability of the part (l,m) lying in the confuser space and being visible:cl,m = 1Z ∫ Ck(dV ) P (EO k (dA))P (El,m(dV1))dV1 (6)where Z is a normalizing factor.
Q7. How do you reduce the complexity of the information theoretic approaches?
These approaches reduce the complexity by annihilating small probabilities [11] or removing weak dependencies [14] and arcs [21].
Q8. What are the goals of a multi-perspective analysis of moving people?
Typical goals of such an analysis are to recover the position, orientation or the pose of each or some subset of the people in the scene.
Q9. What is the error in estimation of person i using the stereo pair?
the error in estimating the position of person i using the stereo pair (k1, k2) is approximated by5In M2Tracker, visibility does not vary with height and hence ground plane analysis of visibility can be performed instead of 3D modelingEi(k1, k2) = (1 − f̃(θk1,k2)Sk1i Sk2i ) (11)where θk1,k2 is the angle between the viewing directions of cameras k1 and k2 on the ground plane.