Multiple Object Tracking with attention to Appearance, Structure, Motion and Size [on] [MASS]

Submitted on 17 Apr. 2019 04:37 by
Handuo Zhang (Nanyang Technological University)

Running time:0.01s

Method Description:
Objective of multiple object tracking (MOT) is to
assign a unique track identity for all the
objects of interest in a video, across the whole
sequence. Tracking-by-detection is the most
common approach used in addressing MOT problem.
In this work, we propose a method to address MOT
by defining a dissimilarity measure based on
object motion, appearance, structure, and size.
We calculate the appearance and structure-based
dissimilarity measure by matching histograms
following a grid architecture. Motion and size
for each track are predicted using the
information from track’s history. These
dissimilarity values are then used in the
Hungarian algorithm, in the data association step
for track identity assignment. In addition, we
introduce a method to address any false detection
in stable tracks. The proposed method runs in
real time following an online approach. We
evaluate our method in both MOT17 benchmark data-
set for pedestrian tracking and KITTI benchmark
data-set for vehicle tracking using the same
system parameters to verify the robustness of the
proposed method. The method can achieve state-of-
the-art results in both benchmarks.
Latex Bibtex:
title={Multiple Object Tracking with attention to
Appearance, Structure, Motion and Size},
author={Karunasekera, Hasith and Wang, Han and
Zhang, Handuo},
journal={IEEE Access},

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.

CAR 85.04 % 85.53 % 85.92 % 88.53 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 89.20 % 97.86 % 93.33 % 33866 742 4101 6.67 % 38824 1030

CAR 74.31 % 22.92 % 2.77 % 301 744

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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