The KITTI Vision Benchmark Suite

OART-XY [OART-XY] [Anonymous Submission] Submitted on 5 Nov. 2026 11:42 by [Anonymous Submission] Running time: 0.1 s Environment: 1 core @ 2.5 Ghz (Python) Method Description: occlusion-aware;lightweight;on-line Parameters: IOU=0.3 MAX_IOU_DISTANCE: 0.70 MAX AGE:80 N_INIT: 2 NN_BUDGET:100 KF_POSITION_WEIGHT: 0.05 KF_VELOCITY_WEIGHT: 0.00625 KF_CONFIDENCE_POWER: 1.0 OUTPUT_MAX STALENESS:0 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 CAR 55.92 % 51.46 % 61.78 % 56.43 % 72.17 % 66.01 % 78.27 % 79.58 % PEDESTRIAN 29.24 % 25.79 % 33.35 % 29.92 % 53.53 % 39.24 % 52.44 % 74.08 % Benchmark TP FP FN CAR 24596 9796 2295 PEDESTRIAN 9201 13949 3737 Benchmark MOTA MOTP MODA IDSW sMOTA CAR 63.36 % 76.11 % 64.84 % 509 46.28 % PEDESTRIAN 21.07 % 69.75 % 23.60 % 587 9.05 % Benchmark MT rate PT rate ML rate FRAG CAR 42.31 % 37.08 % 20.61 % 540 PEDESTRIAN 14.43 % 30.93 % 54.64 % 707 Benchmark # Dets # Tracks CAR 26891 702 PEDESTRIAN 12938 216 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.