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 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 56.61 % 64.28 % 50.55 % 67.37 % 82.77 % 53.96 % 81.97 % 84.26 %
PEDESTRIAN 32.06 % 36.30 % 28.83 % 39.06 % 68.00 % 32.14 % 69.60 % 76.67 %

Benchmark TP FP FN
CAR 27645 6747 350
PEDESTRIAN 12008 11142 1290

Benchmark MOTA MOTP MODA IDSW sMOTA
CAR 77.98 % 82.13 % 79.36 % 475 63.62 %
PEDESTRIAN 44.19 % 72.10 % 46.30 % 488 29.72 %

Benchmark MT rate PT rate ML rate FRAG
CAR 52.46 % 35.08 % 12.46 % 309
PEDESTRIAN 20.27 % 45.02 % 34.71 % 597

Benchmark # Dets # Tracks
CAR 27995 842
PEDESTRIAN 13298 571

This table as LaTeX