Multi-Object Tracking and Segmentation with a Space-Time Memory Network
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We propose a method for multi-object tracking and segmentation that does not require fine-tuning or per benchmark hyper-parameter selection. The proposed tracker, MeNToS, addresses particularly the data association problem. Indeed, the recently introduced HOTA metric, which has a better alignment with the human visual assessment by evenly balancing detections and associations quality, has shown that improvements are still needed for data association. After creating tracklets using instance segmentation and optical flow, the proposed method relies on a space-time memory network developed for one-shot video object segmentation to improve the association of tracklets with temporal gaps. We evaluated our tracker on KITTIMOTS and MOTSChallenge and show the benefit of our data association strategy with the HOTA metric. The project page is <www.mehdimiah.com/mentos+>.
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