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1. Verfasser: Malde, Ketil
Format: Preprint
Veröffentlicht: 2025
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Online-Zugang:https://arxiv.org/abs/2506.17186
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author Malde, Ketil
author_facet Malde, Ketil
contents There now exists many popular object detectors based on deep learning that can analyze images and extract locations and class labels for occurrences of objects. For image time series (i.e., video or sequences of stills), tracking objects over time and preserving object identity can help to improve object detection performance, and is necessary for many downstream tasks, including classifying and predicting behaviors, and estimating total abundances. Here we present yasmot, a lightweight and flexible object tracker that can process the output from popular object detectors and track objects over time from either monoscopic or stereoscopic camera configurations. In addition, it includes functionality to generate consensus detections from ensembles of object detectors.
format Preprint
id arxiv_https___arxiv_org_abs_2506_17186
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle YASMOT: Yet another stereo image multi-object tracker
Malde, Ketil
Computer Vision and Pattern Recognition
There now exists many popular object detectors based on deep learning that can analyze images and extract locations and class labels for occurrences of objects. For image time series (i.e., video or sequences of stills), tracking objects over time and preserving object identity can help to improve object detection performance, and is necessary for many downstream tasks, including classifying and predicting behaviors, and estimating total abundances. Here we present yasmot, a lightweight and flexible object tracker that can process the output from popular object detectors and track objects over time from either monoscopic or stereoscopic camera configurations. In addition, it includes functionality to generate consensus detections from ensembles of object detectors.
title YASMOT: Yet another stereo image multi-object tracker
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2506.17186