ItemArticle

Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications

Title
Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications
Creator
Mohanapriya, D.; Rajalakshmi, K.; Helen Josephine, V.L.; Panneerselvam, Karthick
Description
The activity of the object in question is alerted directly upon completion of an effective object tracking. Dependent on hardware support or not, a strong object tracking protocol is required for a precise object tracking application. According to these methods, tracking an object accurately within a predetermined processing time window required a significant amount of computer complexity. In contrast, a variety of quality-degrading elements, including occlusion, shifting lighting, shadows, and so on, have an adverse effect on tracking. All of these tracking shortcomings will be fixed by a revolutionary residual network based on loss operator and anchor creation. Detection of object has concerns that rely on the process of feature extraction to afford efficient quality. For this purpose a model called ResNet has been used that comprises thirty layers and hence named as Resnet-thirty. These networks are a type of Convolutional Neural Network (CNN) that contain residual connections among various layers. The various merits of these connections is the network has the capability to learn the features of global, local and intermediate in parallel. As such, the system is robust against changes in lighting. These variations in light were understood in terms of tracking objects within a changing background. The proposed work uses MOT datasets. This dataset comprises of MOT 15, MOT16, MOT17 and MOT20. The results have been found by using these datasets. Hence, it evidently outperforms in terms of precision, recall, MOTA, IDF, MOTP, SAIDF and F1 measure to track the objects. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Source
SN Computer Science;Volume;6;Issue;2;Article No.;117;
Date
01-01-2025
Publisher
Springer
Subject
Computer vision; Loss operator; Object tracking; Residual network
Coverage
Mohanapriya D., Department of Computer Science (SF), Sourasthra College, Tamil Nadu, Madurai, India; Rajalakshmi K., Department of Computer Applications, CMR Institute of Technology, Karnataka, Bangalore, India; Helen Josephine V.L., Business Analytics, School of Business and Management, Christ University, Karnataka, Bangalore, India; Panneerselvam K., Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Tamilnadu, Uthupalayam, 641407, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 2662995X;
Format
online
Language
English
Type
Article

Citation

Mohanapriya, D.; Rajalakshmi, K.; Helen Josephine, V.L.; Panneerselvam, Karthick, “Novel Anchor Generation Based Residual Network for Object Tracking in Video-Surveillance Applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22137.

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