Method

MOTSFusion [st][at] [MOTSFusion]
[Anonymous Submission]

Submitted on 28 Mar. 2019 13:43 by
[Anonymous Submission]

Running time:0.44 s
Environment:GPU (Python)

Method Description:
Our method first builds up tracklets with optical
flow segmentation mask consistency in the image
domain, and then uses 3D depth and ego-motion
information to create dynamic 3D reconstructions
of these tracklets. The dynamic reconstructions
are then used to compute the 3D motion of each
tracklet and combine tracklets into long-term
temporally consistent object tracks that can
bridge detection gaps and occlusions.
Parameters:
Detector = RRC
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 84.83 % 85.21 % 85.63 % 88.28 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.76 % 98.02 % 93.16 % 33634 681 4260 6.12 % 38414 1155

Benchmark MT PT ML IDS FRAG
CAR 73.08 % 24.15 % 2.77 % 275 759

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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