Method

Hybrid Motion Model for Multiple Object Tracking in Moving Camera [HMM]


Submitted on 22 Jan. 2022 17:25 by
yubin wu (Beihang university)

Running time:0.04 s
Environment:8 cores @ >3.5 Ghz (Python + C/C++)

Method Description:
Only based on monocular system, we propose a novel
Hybrid Motion Model (HMM) to improve tracking
accuracy in moving camera. First, HMM evaluates
camera motion hypotheses by measuring optical flow
similarity and transition smoothness to perform
robust camera trajectory estimation. Second, along
the camera trajectory, smooth dynamic projection
is used to map object from image to world
coordinate. Third, to deal with inconsistency of
motion, affected by occlusion and interaction with
the increase of time interval, object motion of
tracklets is described by multi-mode motion filter
for adaptive modeling. Fourth, in tracklets
association, we propose a spatio-temporal
evaluation mechanism, which achieves higher
discriminability in motion measurement.
Parameters:
N/A
Latex Bibtex:
N/A

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
PEDESTRIAN 39.97 % 44.34 % 36.41 % 51.33 % 62.62 % 48.06 % 49.13 % 76.11 %

Benchmark TP FP FN
PEDESTRIAN 15791 7359 3185

Benchmark MOTA MOTP MODA IDSW sMOTA
PEDESTRIAN 52.61 % 71.89 % 54.45 % 427 33.43 %

Benchmark MT rate PT rate ML rate FRAG
PEDESTRIAN 36.43 % 40.89 % 22.68 % 628

Benchmark # Dets # Tracks
PEDESTRIAN 18976 243

This table as LaTeX