Benchmark:

MOT15 |

Short name:

TC_ODAL

Detector:

Public

Description:

Online multi-object tracking aims at producing complete tracks of multiple objects using the information accumulated up to the present moment. It still remains a difficult problem in complex scenes, because of frequent occlusion by clutter or other objects, similar appearances of different objects, and other factors. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively.

We first propose the tracklet confidence using the detectability and continuity of a tracklet, and formulate a multi-object tracking problem based on the tracklet confidence. The multi-object tracking problem is then solved by associating tracklets in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive associations. Here, for reliable association between tracklets and detections, we also propose a novel online learning method using an incremental linear discriminant analysis for discriminating the appearances of objects. By exploiting the proposed learning method, tracklet association can be successfully achieved even under severe occlusion. Experiments with challenging public datasets show distinct performance improvement over other batch and online tracking methods.

[Parameters]
show_scan=4
new_thr=5
obs_thr=0.32508
type_thr=0.61126
scale_unit=2.827
std_height=180
pos_var=30, 75
alpha=0.44719
lambda=0.90809
atten=0.85705
init_prob=0.67935
tmplsize=64, 32
Bin=64
vecsize=2048
subregion=1
colortype=HSV
q=3.8414
ppp=4.153
use_ILDA=1
n_update=0
eigenThreshold=0.01
up_ratio=1
duration=5

Reference:

S. Bae, K. Yoon. Robust Online Multi-Object Tracking based on Tracklet Confidence and Online Discriminative Appearance Learning. In CVPR, 2014.

Last submitted:

February 19, 2020 (4 years ago)

Published:

November 01, 2014 at 03:09:33 CET

Submissions:

2

Project page / code:

https://cvl.gist.ac.kr/project/cmot.html

Open source:

Yes

Hardware:

2.6 GHz, 16 Cores

Runtime:

1.5 Hz

Benchmark performance:

Detailed performance:

Raw data:

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