Method

Learned Object Descriptors [LOD]
[Anonymous Submission]

Submitted on 23 Apr. 2025 16:49 by
[Anonymous Submission]

Running time:0.1 s
Environment:GPU @ 2.0 Ghz (Python)

Method Description:
This method relies only on the visual appearance of objects. We use a YOLO model to get detections and ConvNeXt model to get reID features from each detection. We then assign track IDs based on the highest cosine similarity between incoming reID features and features from all previous frames. If any duplicate IDs are detected within a frame, then only the detecion with highest similarity score is kept and all other duplicates are rejected.
Parameters:
The similarity threshold \tau=0.82
Yolo confidence threshold=0.5
Latex Bibtex:

Detailed Results

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 63.18 % 58.71 % 68.49 % 60.84 % 85.52 % 72.91 % 85.02 % 86.34 %
PEDESTRIAN 38.00 % 31.55 % 45.91 % 33.00 % 74.80 % 49.43 % 74.64 % 80.16 %

Benchmark TP FP FN
CAR 24074 10318 391
PEDESTRIAN 9693 13457 521

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 67.29 % 84.77 % 68.86 % 542 56.63 %
PEDESTRIAN 38.52 % 76.52 % 39.62 % 255 28.69 %

Benchmark MT rate PT rate ML rate FRAG
CAR 43.85 % 39.85 % 16.31 % 465
PEDESTRIAN 15.46 % 38.83 % 45.70 % 585

Benchmark # Dets # Tracks
CAR 24465 1040
PEDESTRIAN 10214 396

This table as LaTeX

This figure as: png pdf

This figure as: png pdf

[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.


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