A hybrid optimisation enabled deep learning for object detection and multi-object tracking
by J. Thirumalai; M. Gomathi; T.S. Sindhu; A. Senthil Kumar; R. Puviarasi
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 46, No. 3, 2024

Abstract: The potential of multi-object tracking (MOT) in academia and industry has drawn growing attention. Despite the various methods that have been suggested to address this issue, it continues to be difficult because of things like sudden changes in appearance and severe object occlusions. In this paper, a Jaya political search optimisation (Jaya-PSO) enabled ShuffleNet is developed for object detection (OD) and MOT. Initially, the input video is fed to video frame extraction. The extracted frames are fed into the object segmentation phase, where the segmentation is done by the mask-regional convolutional neural network (Mask-RCNN), trained by tangent squirrel search optimisation (TSSO). Here, TSSO is the integration of the tangent search algorithm (TSA) and squirrel search optimisation (SSO). Then, the object recognition is performed using ShuffleNet trained by Jaya-PSO, where the Jaya-PSO is from the Jaya algorithm, political optimiser (PO) and TSA. Finally, MOT is done by the Henry gas solubility optimised unscented Kalman filtering (HGSO-based UKF). The HGSO-based UKF is the integration of Henry gas solubility optimisation (HGSO) and unscented Kalman filtering (UKF). The measures utilised for analysis are accuracy, sensitivity, specificity and multiple object tracking precision (MOTP). The proposed method attained 92.9% accuracy, 92.1% sensitivity, 92.9% specificity, and 91.0% MOTP.

Online publication date: Mon, 15-Jul-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com