The KITTI Vision Benchmark Suite

AnonTrack [gp] [on] [at] [AnonTrack] [Anonymous Submission] Submitted on 28 Oct. 2026 11:46 by [Anonymous Submission] Running time: 0.02 s Environment: GPU @ >3.5 Ghz (Python) Method Description: TBD Parameters: None Latex Bibtex:

From all 29 test sequences, our benchmark computes the HOTA tracking metrics (HOTA, DetA, AssA, DetRe, DetPr, AssRe, AssPr, LocA) [1] as well as the CLEARMOT, MT/PT/ML, identity switches, and fragmentation [2,3] metrics. The tables below show all of these metrics.

Benchmark HOTA DetA AssA DetRe DetPr AssRe AssPr LocA PEDESTRIAN 54.91 % 51.02 % 59.34 % 55.00 % 72.54 % 63.95 % 74.49 % 78.58 % Benchmark TP FP FN PEDESTRIAN 16498 6652 1053 Benchmark MOTA MOTP MODA IDSW sMOTA PEDESTRIAN 66.20 % 74.56 % 66.72 % 119 48.08 % Benchmark MT rate PT rate ML rate FRAG PEDESTRIAN 39.86 % 37.80 % 22.34 % 462 Benchmark # Dets # Tracks PEDESTRIAN 17551 268 This table as LaTeX

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[1] J. Luiten, A. Os̆ep, P. Dendorfer, P. Torr, A. Geiger, L. Leal-Taixé, B. Leibe: HOTA: A Higher Order Metric for Evaluating Multi-object Tracking. IJCV 2020.
[2] K. Bernardin, R. Stiefelhagen: Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics. JIVP 2008.
[3] Y. Li, C. Huang, R. Nevatia: Learning to associate: HybridBoosted multi-target tracker for crowded scene. CVPR 2009.