What is in front? Multiple-object detection and tracking with dynamic occlusion handling


by J Tao, M Enzweiler, U Franke, D Pfeiffer, R Klette
Abstract:
This paper proposes a multiple-object detection and tracking method that explicitly handles dynamic occlusions. A context-based multiple-cue detector is proposed to detect occluded vehicles (occludees). First, we detect and track fully-visible vehicles (occluders). Occludee detection adopts those occluders as priors. Two classifiers for partiallyvisible vehicles are trained to use appearance cues. Disparity is adopted to further constrain the occludee locations. A detected occludee is then tracked by a Kalman-based tracking-by-detection method. As dynamic occlusions lead to role changes for occluder or occludee, an integrative module is introduced for possibly switching occludee and occluder trackers. The proposed system was tested on overtaking scenarios. It improved an occluder-only tracking system by over 10% regarding the frame-based detection rate, and by over 20% regarding the trajectory detection rate. The occludees are detected and tracked in the proposed method up to 7 seconds before they are picked up by occluder-only method.
Reference:
What is in front? Multiple-object detection and tracking with dynamic occlusion handling (J Tao, M Enzweiler, U Franke, D Pfeiffer, R Klette), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, volume 9256, 2015.
Bibtex Entry:
@inproceedings{tao2015whathandling,
author = "Tao, J and Enzweiler, M and Franke, U and Pfeiffer, D and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
organization = "Valletta, Malta",
pages = "14--26",
publisher = "Springer Verlag",
title = "What is in front? Multiple-object detection and tracking with dynamic occlusion handling",
volume = "9256",
year = "2015",
abstract = "This paper proposes a multiple-object detection and tracking method that explicitly handles dynamic occlusions. A context-based multiple-cue detector is proposed to detect occluded vehicles (occludees). First, we detect and track fully-visible vehicles (occluders). Occludee detection adopts those occluders as priors. Two classifiers for partiallyvisible vehicles are trained to use appearance cues. Disparity is adopted to further constrain the occludee locations. A detected occludee is then tracked by a Kalman-based tracking-by-detection method. As dynamic occlusions lead to role changes for occluder or occludee, an integrative module is introduced for possibly switching occludee and occluder trackers. The proposed system was tested on overtaking scenarios. It improved an occluder-only tracking system by over 10% regarding the frame-based detection rate, and by over 20% regarding the trajectory detection rate. The occludees are detected and tracked in the proposed method up to 7 seconds before they are picked up by occluder-only method.",
doi = "10.1007/978-3-319-23192-1_2",
startyear = "2015",
startmonth = "Sep",
startday = "2",
finishyear = "2015",
finishmonth = "Sep",
finishday = "4",
isbn = "9783319231914",
issn = "0302-9743",
eissn = "1611-3349",
language = "eng",
conference = "16th International Conference on Computer Analysis of Images and Patterns",
}