Method

Multi-Class Multi-Object Tracking using Changing Point Detection [MCMOT-CPD]


Submitted on 31 Aug. 2016 04:44 by
Byungjae Lee (Inha University)

Running time:0.01 s
Environment:1 core @ 3.5 Ghz (Python)

Method Description:
This method presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multi-object tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes due to a drift and an occlusion based spatiotemporal characteristics of track states. The ensemble of convolutional neural network (CNN) based object detector and Lucas-Kanede Tracker (KLT) based motion detector is employed to compute the likelihoods of foreground regions as the detection responses of different object classes.
Parameters:
n/a
Latex Bibtex:
@InProceedings{Lee2016ECCVWORK,
Title = {Multi-class Multi-object Tracking Using Changing Point Detection},
Author = {Byungjae Lee and Enkhbayar Erdenee and SongGuo Jin and Mi Young Nam and Young Giu Jung and Phill{-}Kyu Rhee},
Booktitle = ECCVWORK,
Year = {2016},

File = {Lee2016ECCVWORK.pdf:Lee2016ECCVWORK.pdf:PDF}
}

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 78.90 % 82.13 % 79.56 % 86.37 %
PEDESTRIAN 45.94 % 72.44 % 46.56 % 91.78 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 81.84 % 98.97 % 89.59 % 30247 316 6713 2.84 % 32831 985
PEDESTRIAN 52.33 % 90.64 % 66.35 % 12200 1260 11112 11.33 % 14725 635

Benchmark MT PT ML IDS FRAG
CAR 52.31 % 36.00 % 11.69 % 228 536
PEDESTRIAN 20.62 % 45.02 % 34.36 % 143 764

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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