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Inhalt des Dokuments

Probability Hypothesis Density (PHD) Multi-Object-Tracking Filter

Lupe

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...

Dissertation

Volker Eiselein, "Pedestrian Tracking-by-Detection for Video Surveillance Applications", 20.05.2019

Assessing Post-Detection Filters for a Generic Pedestrian Detector in a Tracking-By-Detection Scheme

Lupe

Tracking-by-detection becomes more and more popular for visual pedestrian tracking applications. However, it requires accurate and reliable detections in order to obtain good results. In this work, we propose two different post-detection filters designed to enhance the performance of custom person detectors. Using a popular deformableparts-based pedestrian detector as a baseline, a detailed comparison over multiple test videos is performed and the gain of both algorithms is proven. Further analysis shows that the improved detection outcomes also lead to improved tracking results. We thus found that the usage of the proposed post-detection filters is recommendable as they do not impose a high computational load and are not limited to a specific detector method

Publication

Eiselein, V., Bochinski, E., and Sikora,T. Assessing Post-Detection Filters for a Generic Pedestrian Detector in a Tracking-By-Detection Scheme. AVSS, 2017

Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data

Lupe

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.

Publication

Kutschbach, T., Bochinski, E., Eiselein, V., and Sikora,T. Sequential Sensor Fusion Combining Probability Hypothesis Density and Kernelized Correlation Filters for Multi-Object Tracking in Video Data . AVSS, 2017

A motion-enhanced Hybrid Probability Hypothesis Density filter for real-time Multi-Human Tracking in video surveillance scenarios

Lupe

The Probability Hypothesis Density (PHD) filter is a multiobject Bayes filter which has been recently becoming popular in the tracking community especially for its linear complexity and its ability to filter out a high amount of clutter. However, its application to Computer Vision scenarios can be difficult as it requires high detection probabilities. Many human detectors suffer from a significant miss-match rate which causes problems for the PHD filter. This article presents an implementation of a Gaussian Mixture PHD (GM-PHD) filter which is enhanced by Optical Flow information in order to account for missed detections

Publication

Eiselein, V., Senst, T., Keller, I., M., and Sikora,T. A motion-enhanced Hybrid Probability Hypothesis Density filter for real-time Multi-Human Tracking in video surveillance scenarios. AVSS, 2013

Real-time Multi-Human Tracking using a Probability Hypothesis Density Filter and multiple detectors

Lupe

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.

Publication:

Eiselein, V., Arp, D., Pätzold, M., and Sikora,T. Real-time Multi-Human Tracking using a Probability Hypothesis Density Filter and multiple detectors. AVSS, 2012

Zusatzinformationen / Extras