Quasi-Polynomial Time Approximation Schemes for Target Tracking

TL;DR

This paper introduces quasi-polynomial time approximation schemes for a target tracking problem involving camera pairs, improving solution accuracy for two objective functions related to tracking angles and distance ratios.

Contribution

It presents quasi-polynomial algorithms achieving near-optimal solutions for target tracking with cameras on a line, surpassing previous polynomial-time approximations.

Findings

Achieves (1-ε)-approximation for maximizing tracking angles.

Achieves (1+ε)-approximation for minimizing distance ratios.

Provides quasi-polynomial algorithms for specific target tracking objectives.

Abstract

We consider the problem of tracking $n$ targets in the plane using $2n$ cameras. We can use two cameras to estimate the location of a target. We are then interested in forming $n$ camera pairs where each camera belongs to exactly one pair, followed by forming a matching between the targets and camera pairs so as to best estimate the locations of each of the targets. We consider a special case of this problem where each of the cameras are placed along a horizontal line $l$, and we consider two objective functions which have been shown to give good estimates of the locations of the targets when the distances between the targets and the cameras are sufficiently large. In the first objective, the value of an assignment of a camera pair to a target is the tracking angle formed by the assignment. Here, we are interested in maximizing the sum of these tracking angles. A polynomial time 2-approximation is known for this problem. We give a quasi-polynomial time algorithm that returns a solution whose value is at least a $(1-\epsilon)$ factor of the value of an optimal solution for any $\epsilon > 0$. In the second objective, the cost of an assignment of a camera pair to a target is the ratio of the vertical distance between the target and $l$ to the horizontal distance between the cameras in the camera pair. In this setting, we are interested in minimizing the sum of these ratios. A polynomial time 2-approximation is known for this problem. We give a quasi-polynomial time algorithm that returns a solution whose value is at most a $(1+\epsilon)$ factor of the value of an optimal solution for any $\epsilon > 0$.