Real-Time and Online Segmentation Multi-Target Tracking with Track Revival Re-Identification

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Martin Ahrnbom, Mikael Nilsson, Håkan Ardö

Published: 01 Jan 2021, Last Modified: 12 Nov 2025Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and ApplicationsEveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: The first online segmentation multi-target tracking algorithm with reported real-time speeds is presented. Based on the popular and fast bounding box based tracker SORT, our method called SORTS is able to utilize segmentations for tracking while keeping the real-time speeds. To handle occlusions, which neither SORT nor SORTS do, we also present SORTS+RReID, an optional extension which uses ReID vectors to revive lost tracks from SORTS to handle occlusions. Despite only computing ReID vectors for 6.9% of the detections, ID switches are decreased by 45%. We evaluate on the MOTS dataset and run at 54.5 and 36.4 FPS for SORTS and SORT+RReID respectively, while keeping 78-79% of the sMOTSA of the current state of the art, which runs at 0.3 FPS. Furthermore, we include an experiment using a faster instance segmentation method to explore the feasibility of a complete real-time detection and tracking system. Code is available: https://github.com/ahrnbom/sorts.