Submitted on 19 Sep. 2017 19:43 by
Krishna Murthy Jatavallabhula (International Institute of Information Technology, Hyderabad, India)

Running time:0.3 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
This paper introduces geometry and object shape
and pose costs for multi-object tracking in urban
driving scenarios. Using images from a monocular
camera alone, we devise pairwise costs for object
tracks, based on several 3D cues such as object
pose, shape, and motion. The proposed costs are
agnostic to the data association method and can
be incorporated into any optimization framework
to output the pairwise data associations. These
costs are easy to implement, can be computed in
real-time, and complement each other to account
for possible errors in a tracking-by-detection
framework. We perform an extensive analysis of
the designed costs and empirically demonstrate
consistent improvement over the state-of-the-art
under varying conditions that employ a range of
object detectors, exhibit a variety in camera and
object motions, and, more importantly, are not
reliant on the choice of the association
framework. We also show that, by using the
simplest of associations frameworks (two-frame
Hungarian assignment), we surpass the state-of-
the-art in multi-object-tracking on road scenes.
More qualitative and quantitative results can be
found at the following URL:
Latex Bibtex:
title={Beyond Pixels: Leveraging Geometry and
Shape Cues for Online Multi-Object Tracking},
author={Sharma, Sarthak and Ansari, Junaid Ahmed
and Krishna Murthy, J. and Madhava Krishna, K.},
booktitle = {Proceedings of the IEEE Conference
on Intelligent Robots and Systems (In Press)},

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 84.24 % 85.73 % 85.60 % 88.62 %

Benchmark recall precision F1 TP FP FN FAR #objects #trajectories
CAR 88.80 % 97.95 % 93.15 % 33656 705 4247 6.34 % 38507 2382

CAR 73.23 % 24.00 % 2.77 % 468 944

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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