Robust Real-Time Multiple Target Tracking

We propose a novel efficient algorithm for robust tracking of a fixed number of targets in real-time with low failure rate. The method is an instance of Sequential Importance Resampling filters approximating the posterior of complete target configurations as a mixture of Gaussians. Using predicted target positions by Kalman filters, data associations are sampled for each measurement sweep according to their likelihood allowing to constrain the number of associations per target. Updated target configurations are weighted for resampling pursuant to their explanatory power for former positions and measurements. Fixed-lag of the resulting positions increases the tracking quality while smart resampling and memoization decrease the computational demand. We present both, qualitative and quantitative experimental results on two demanding real-world applications with occluded and highly confusable targets, demonstrating the robustness and real-time performance of our approach outperforming current state-of-the-art.