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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi
Ignatius Aris Wibowo
Institut Teknologi Sepuluh Nopember, Surabaya
Computer Science & IT Dentistry Education Library & Information Science

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Density-aware reliability association for training-free FDTA enhancement in multi-object tracking Ignatius Aris Wibowo; Hilmil Pradana; Ahmad Saikhu
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1594

Abstract

Multi-object tracking (MOT) in crowded scenes is difficult because objects frequently occlude each other and share similar appearances, leading to identity confusion. End to-end trackers based on the From Detection to Association (FDTA) architecture reduce inter-object embedding errors but remain susceptible to ID Switches (IDSW) when ID confidence falls briefly during crowd traversal. This paper proposes a training-free density-aware association module that operates on top of a frozen FDTA model at inference time. Identity recovery activates only when both local density and frame-level detection count exceed fixed thresholds, preventing false merges in sparse regions. The method also includes a geometry-based offline tracklet linker that reconnects fragmented trajectories using bounding box constraints alone, with no learned components. On the DanceTrack validation set, the approach reduces IDSW by 13% and raises Higher Order Tracking Accuracy (HOTA) from 64.36 to 65.39, Association Accuracy (AssA) to 57.15, and ID F1 Score (IDF1) to 71.47. On the official test server, the method scores 72.01 HOTA and 78.21 IDF1
Co-Authors Ahmad Saikhu Hilmil Pradana