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<p>This research presents a comprehensive framework for advancing moving object segmentation in surveillance applications through four critical phases: (a) data pre-processing, (b) moving object segmentation, (c) feature extraction, and (d) object classification, tailored for the unique demands of surveillance contexts. The data pre-processing phase incorporates cutting-edge techniques including video-to-frame conversion, image enhancement using Histogram Equalization, noise reduction with Median Filtering, and dynamic scaling for optimal object preservation and computational efficiency. In the moving object segmentation phase, introduce SemSegX, a state-of-the-art deep learning method combining Vision Transformers, Efficient Nets, and DeepLabv4 for precise and efficient segmentation. Advanced feature extraction techniques encompass 3D convolutional neural networks, HOG, SIFT, edge-based features, and Zernike Moments. Propose Optimal Feature Selection through Hybrid Avian-Crustacean Optimization (HACO) by combining Artificial Hummingbird Algorithm and Crayfish Optimization Algorithm to streamline feature identification. For object classification, using TripForceNet, integrating Efficient Net, a MobileNetV3, and a custom-optimized ResNet, leveraging late fusion and attention mechanisms for enhanced accuracy. Implemented in Python, this model promises to elevate surveillance object segmentation and classification to new heights.</p>

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Moving Object Tracking for Surveillance Application Using Semantic Segmentation Excellence (SemSegX) and TripForceNet

  • 期刊论文

  • 发表日期：

    2025年9月24日

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  • Dipika Gupta,
  • Manish Kumar

摘要

This research presents a comprehensive framework for advancing moving object segmentation in surveillance applications through four critical phases: (a) data pre-processing, (b) moving object segmentation, (c) feature extraction, and (d) object classification, tailored for the unique demands of surveillance contexts. The data pre-processing phase incorporates cutting-edge techniques including video-to-frame conversion, image enhancement using Histogram Equalization, noise reduction with Median Filtering, and dynamic scaling for optimal object preservation and computational efficiency. In the moving object segmentation phase, introduce SemSegX, a state-of-the-art deep learning method combining Vision Transformers, Efficient Nets, and DeepLabv4 for precise and efficient segmentation. Advanced feature extraction techniques encompass 3D convolutional neural networks, HOG, SIFT, edge-based features, and Zernike Moments. Propose Optimal Feature Selection through Hybrid Avian-Crustacean Optimization (HACO) by combining Artificial Hummingbird Algorithm and Crayfish Optimization Algorithm to streamline feature identification. For object classification, using TripForceNet, integrating Efficient Net, a MobileNetV3, and a custom-optimized ResNet, leveraging late fusion and attention mechanisms for enhanced accuracy. Implemented in Python, this model promises to elevate surveillance object segmentation and classification to new heights.