Problem
We are tracking a number of targets over time and obtain several observations. How do we know which observations come from which target?
This is a common problem in many applications including air traffic control, self-driving vehicles, computer vision, etc.
Objective
Create mappings between targets and observations
Each mapping is assigned a score
Select the mappings with highest scores
Solvable for single-sensor tracking
NP-hard for multi-sensor tracking
Solution
Existing methods:
Novel methods:
These four methods can be classifed in two ways:
Exact score | Approximate score | |
Deterministic | Sequential | Combination |
Stochastic sampling | Cross entropy | Gibbs |
Reference:
1: F. Papi, "Multi-sensor $\delta$-GLMB filter for multi-target tracking using Doppler only measurements,” in Europen Intell. Security Inform. Conf., Sept. 2015, pp. 83–89
2: B. N. Vo and B. Vo, "Multi-sensor multi-object tracking with the generalized labeled multi-Bernoulli filter," available on arxiv
Results
We implement the $\delta$-generalized labeled multi-Bernoulli filter and use the four algorithms to generate data associations
Fig. 1 True target tracks (blue dots) and sensor positions (red crosses)
Fig. 2 Average OSPA with respect to measurement noise level
The overall tracking performance degrades as the measurement noise level increases. The cross entropy method has consistenly the best tracking performance followed by the sequential methods. The combination and cross entropy methods have the worst performance as they use the approximate ranking function.
Fig. 3 Average runtime with respect to measurement noise level
The sequential and combination methods have fairly consistent runtime regardless of measurement noise level. For the Gibbs and cross entropy methods, the stochastic sampling generates more maps and by extension higher workload when the measurement noise increases. Therefore, the total runtime increases linearly with measurement noise level.
"Algorithms for the Multi-Sensor Assignment Problem in the Delta-Generalized Labeled multi-Bernoulli Filter", IEEE 7th CAMSAP, Accepted, 2017.
"Multi-Sensor Data Assignment Algorithms in the Delta-Generalized Labeled multi-Bernoulli Filter", IEEE Trans. Signal Proc., Submitted, 2017.