Towards Robust Online Multi-Object Tracking

MPhil Thesis Defence


Title: "Towards Robust Online Multi-Object Tracking"

By

Mr. Jeongseok HYUN


Abstract

Online multi-object tracking (MOT) is one of the fundamental tasks in 
computer vision with its wide range of applications in video surveillance 
and autonomous driving. However, the online setting is challenging and not 
robust against occlusion and motion blur in the videos since future 
information is restricted to be exploited to refine the output from the 
current timestep. In this thesis, we propose two online MOT models which 
are robust against partial occlusion by approaching two different 
spatio-temporal (S-T) modeling: 1) pixel-level S-T modeling, and 2) 
object-level S-T modeling.

In the approach of pixel-level S-T modeling, we propose Dynamic GNNs for 
Simultaneous Detection and Tracking (DynGSDT) that enhances the feature 
map of the current frame by dynamically propagating the previous tracklets 
to the current frame. With learned edge weights in GNN, the current frame 
adaptively selects the features from the previous frame. Experiment 
results show that DynGSDT outperforms its baseline models FairMOT and 
GSDT. Especially, DynGSDT shows a larger gap on MOT20 than MOT17 since 
MOT20 is much more crowded than MOT17 and thus occlusion between objects 
is dominant.

We point out that the existing tracking-by-detection (TBD) framework is 
inherently vulnerable to missed detections caused by occlusion. Since only 
detections whose confidence score is above the detection threshold are 
selected for tracking in the TBD framework, the object under severe 
occlusion may be detected with a score slightly lower than the threshold 
and is excluded from tracking. Motivated by this problem, we suggest 
detection recovery by tracking framework and propose Sparse Graph Tracker 
(SGT) based on object-level S-T modeling with GNN. SGT associates 
tracklets and top-$K$ detections. Then, the missed detections whose score 
is lower than the threshold are recovered as positive detections if they 
are matched with the tracklets. SGT achieves the state-of-the-art 
performance on the MOT20 dataset and comparable performance on the 
MOT16/17 datasets. Extensive ablation studies demonstrate the 
effectiveness of the detection recovery mechanism proposed in SGT.


Date:  			Tuesday, 2 August 2022

Time:			9:00am - 11:00am

Zoom Meeting:
https://hkust.zoom.us/j/95877658018?pwd=aWlpeHI1UHhQMmNmVVBXTEtocW1wUT09

Committee Members:	Prof. Dit-Yan Yeung (Supervisor)
 			Prof. Tong Zhang (Chairperson)
 			Dr. Qifeng Chen
 			Dr. Dan Xu


**** ALL are Welcome ****