Tracking-by-detection is a common approach to multiobject tracking. With ever increasing performances of object detectors, the basis for a tracker becomes much more reliable. In combination with commonly higher frame rates, this poses a shift in the challenges for a successful tracker. That shift enables the deployment of much simpler tracking algorithms which can compete with more sophisticated approaches at a fraction of the computational cost. We present such an algorithm and show with thorough experiments its potential using a wide range of object detectors. The proposed method can easily run at 100K fps while outperforming the state-of-the-art on the DETRAC vehicle tracking dataset.

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The Probability Hypothesis Density (PHD) filter is a multi-object Bayes filter which has recently attracted a lot of interest in the tracking community mainly for its linear complexity and its ability to deal with high clutter especially in radar/sonar scenarios. In the computer vision community however, underlying constraints are different from radar scenarios and have to be taken into account when using the PHD filter. In this article, we propose a new tree-based path extraction algorithm for a Gaussian Mixture PHD filter in Computer Vision applications. We also investigate how an additional benefit can be achieved by using a second human detector and justify an approximation for multiple sensors in low-clutter scenarios.

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This work applies the Gaussian Mixture Probability Hypothesis Density (GMPHD) Filter to multi-object tracking in video data. In order to take advantage of additional visual information, Kernelized Correlation Filters (KCF) are evaluated as a possible extension of the GMPHD tracking-by-detection scheme to enhance its performance. The baseline GMPHD filter and its extension are evaluated on the UA-DETRAC benchmark, showing that combining both methods leads to a higher recall and a better quality of object tracks to the cost of increased computational complexity and increased sensitivity to false-positives.