Method

PB-MOT++：Extending Pose-aware Association to Trajectory-Centric Offline Optimization [PB-MOT++]


Submitted on 30 Mar. 2026 17:00 by
Bo Pang (Zhejiang University)

Running time:4e-4 s
Environment:>8 cores @ 3.0 Ghz (Python)

Method Description:
PB-MOT++, a computationally efficient trajectory-
centric offline optimization framework that
extends our previous pose-aware tracker, PB-MOT.
Instead of repeatedly operating on frame-wise
detections, PB-MOT++ directly optimizes
trajectories through an evidence-first four-stage
pipeline: boundary recovery, topology repair, gap
completion, and kinematic refinement. By
separating structural restoration from geometric
smoothing, the proposed framework effectively
reduces error propagation and improves long-range
trajectory consistency.
Parameters:
N/A
Latex Bibtex:
N/A

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 92.55 % 86.73 % 92.59 % 89.42 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 96.60 % 96.85 % 96.73 % 37638 1224 1324 11.00 % 44402 774

Benchmark MT PT ML IDS FRAG
CAR 88.15 % 7.69 % 4.15 % 14 55

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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