International Journal of Emerging Research in Engineering, Science, and Management
Vol. 5, Issue 2, pp. 23-35, Apr-Jun 2026.
https://doi.org/10.58482/ijeresm.v5i2.2

Received: 21 Jan 2026 | Revised: 10 Apr 2026 | Accepted: 21 Apr 2026 | Published: 28 Apr 2026

This work is licensed under a Creative Commons Attribution 4.0 International License.

Feature Fusion Strategy Based on Concatenation of Histogram of Oriented Gradients and Pretrained CNN Features for Visual Object Tracking

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1Villari Sreenatha Sarma

2P.M. Ashok Kumar

3Vadamala Purandhar Reddy

1,2Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India.

1,3Audisankara College of Engineering & Technology, Gudur, Andhra Pradesh, India.

Abstract: This paper presents a hybrid feature fusion approach for visual object tracking that combines hand-crafted gradient-based descriptors with semantic representations extracted from a pretrained convolutional neural network. The proposed tracker integrates Histogram of Oriented Gradients (HOG) features with ResNet-50-based deep features within a correlation filter framework to improve robustness against appearance variations, occlusion, and illumination changes. A concatenation-based fusion strategy with adaptive confidence-driven weighting is incorporated to dynamically balance the contribution of handcrafted and deep features during tracking. The architecture employs parallel feature extraction branches and multi-scale feature integration to enhance localization performance while maintaining computational efficiency. Experimental evaluation on standard benchmark datasets, including OTB-2015 and related tracking sequences, demonstrates that the proposed fusion strategy provides improved performance compared with individual feature-based tracking approaches and achieves competitive results relative to baseline correlation-filter trackers under challenging conditions. The study also outlines potential directions for further enhancement through online adaptation and attention-based feature fusion strategies.

Keywords: Visual Object Tracking, Feature Fusion, Histogram of Oriented Gradients, Correlation Filter Tracking, Adaptive Feature Weighting.

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