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Method

SMF-Tracker-CasA [SMF-Tracker-CasA]
[Anonymous Submission]

Submitted on 10 Nov. 2026 02:17 by
[Anonymous Submission]

Running time:1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
The proposed distributed ellipsoidal set-membership tracker estimates each object’s 3D motion, size, and heading with separate filters, reducing the conservatism of a single joint filter. Each filter alternates between prediction and measurement updates while representing bounded estimation errors as ellipsoids. Tracks are matched to detections first in bird’s-eye view and then, for unmatched objects, in a virtual camera’s range view.
Parameters:
input_score: 0.65
bev_cost_thre: 1.5
rv_cost_thre: -0.2
Latex Bibtex:

Detailed Results

From all 29 test sequences, our benchmark computes the commonly used tracking metrics CLEARMOT, MT/PT/ML, identity switches, and fragmentations [1,2]. The tables below show all of these metrics.


Benchmark MOTA MOTP MODA MODP
CAR 87.67 % 86.98 % 87.76 % 89.52 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 92.82 % 95.98 % 94.38 % 35309 1479 2730 13.30 % 41620 1358

Benchmark MT PT ML IDS FRAG
CAR 79.85 % 17.38 % 2.77 % 31 502

This table as LaTeX

[1] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[2] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.


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