Robust and Accurate Object Tracking Under Various Types of Occlusions

Pan, Jiyan; Hu, Bo; Zhang, Jian Qiu

doi:10.1109/tcsvt.2007.913975

Cited by 37 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, we review a few existing works that specifically deal with the occlusion. The Content-Adaptive Progressive Occlusion Analysis (CAPOA) algorithm [16] by Jiyan et al distinguished the target and outliers by combining the information provided by spatiotemporal context, reference target, and motion constraint. Accurate tracking of an occluded target was achieved by refining the target location using Variant Mask Template Matching (VMTM).…”

Section: Literature Reviewmentioning

confidence: 99%

GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

Dasari,

Gorthi

2023

Preprint

View full text Add to dashboard Cite

Occlusionis a frequent phenomenon that hinders the task of visual object tracking. Since occlusion can be from any object and in any shape, data augmentation techniques will not greatly help identify or mitigate the tracker loss. Some of the existing works deal with occlusion only in an unsupervised manner. This paper proposes a generic deep learning framework for identifying occlusion in a given frame by formulating it as a supervised classification task for the first time. The proposed architecture introduces an ''occlusion classification" branch into supervised trackers. This branch helps in the effective learning of features and also provides occlusion status for each frame. A metric is proposed to measure the performance of trackers under occlusion at frame level. The efficacy of the proposed framework is demonstrated on two supervised tracking paradigms: One is from the most commonly used Siamese region proposal class of trackers, and another from the emerging Transformer-based trackers. This framework is tested on six diverse datasets (GOT-10k, LaSOT, OTB2015, TrackingNet, UAV123, and VOT2018), and it achieved significant improvements in performance over the corresponding baselines while performing on par with the state-of-the-art trackers. The contributions in this work are more generic, as any supervised tracker can easily adopt them.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

Dasari,

Gorthi

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Although many effective visual target tracking methods have been proposed, there are still a lot of difficulties in designing a robust tracking algorithm because of challenging complex scenarios such as significant illumination change in natural environment, pose variations of the objects and non-linear deformations of shapes and noise and dense clutters in complex background etc. [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Dezert–Smarandache theory for multiple targets tracking in natural environment

Wang

Fang

Zhang

et al. 2013

IET Computer Vision

View full text Add to dashboard Cite

The aim of this article was to investigate multiple targets tracking in natural environment based on Dezert-Smarandache theory (DSmT). On the basis of establishing conflict strategy and combination model, the basic framework and algorithm of fusing multi-source information were described. The multiple targets tracking platform which embedded location and colour cues into the particle filters (PFs) was developed in the framework of DSmT. Three sets of experiments with comparisons were carried out to validate the suggested tracking approach. Results showed that the conflict strategy and DSmT combination model were available, and the introduced approach exhibited a significantly better performance for dealing with high conflict between evidences than a PF. As a result, the approach was suitable for real-time video-based targets tracking, and it had the ability to track interesting targets. Furthermore, the approach can easily be generalised to deal with larger number of targets and additional cues in a complicated environment.

show abstract

Perception for Detection and Grasping

Guerra

Pumarola

Grau

et al. 2019

Springer Tracts in Advanced Robotics

View full text Add to dashboard Cite

This chapter presents a methodology for the detection of the crawler used in the project AEROARMS. The approach consisted on using a two-step progressive strategy, going from rough detection and tracking, for approximation maneuvers, to an accurate positioning step based on fiducial markers. Two different methods are explained for the first step, one using efficient image segmentation approach; and the second one using Deep Learning techniques to detect the center of the crawler. The fiducial markers are used for precise localization of the crawler in a similar way as explained in chapter four. The methods can run in real-time.

show abstract

Robust and Accurate Object Tracking Under Various Types of Occlusions

Cited by 37 publications

References 26 publications

GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

GOA-Net: Generic Occlusion Aware Networks for Visual Tracking

Dezert–Smarandache theory for multiple targets tracking in natural environment

Perception for Detection and Grasping

Contact Info

Product

Resources

About