A robust attribute-aware and  real-time multi-target multi-camera tracking system using multi-scale enriched features and hierarchical clustering

Moghaddam, Mahnaz; Charmi, Mostafa; Hassanpoor, Hossein

doi:10.1007/s11554-023-01301-y

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…However, traditional hierarchical clustering strategies have inherent limitations, such as difficulty determining the optimal number of clusters, selecting appropriate parameter settings, and handling large datasets. There has been an increase in the adoption of more advanced evaluation techniques for hierarchical clustering, such as integrated deep learning [2]. Deep learning is a subfield of machine learning that focuses on extracting meaningful patterns from unlabeled data through the analysis of inherent representations.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Park

2024

JMC

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Machine learning is an effective technique for optimizing real-time electronics product data analysis. It can efficiently handle large electronics product datasets, reducing processing time and resource requirements for generating insights. This study assesses the current status of methods and applications for optimizing real-time data analysis by examining existing research in machine learning-based recommendation systems for electronic products. The indicated subjects encompass using machine learning algorithms to discern characteristics and correlations from large datasets, applying machine learning for prognostic analytics and projection, and utilizing machine learning to identify anomalies. The paper provides examples of machine learning-based evaluation optimization solutions that focus on utilizing unorganized data and delivering real-time dashboards. Presented here is a discussion on the complex challenges and potential benefits associated with utilizing machine learning to optimize real-time data processing. Machine learning may efficiently expedite real-time data assessment while delivering precise and timely outcomes

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Section: Introductionmentioning

confidence: 99%

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Park

2024

JMC

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Tree-based indexing technique for efficient and real-time label retrieval in the object tracking system

et al. 2023

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Learning to Track With Dynamic Message Passing Neural Network for Multi-Camera Multi-Object Tracking

Bilakeri,

Kotegar

2024

IEEE Access

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Multi-camera multi-object tracking (MC-MOT) has become pivotal in various real-world applications within computer vision. Despite extensive research, solving the data association problem remains one of the most formidable challenges in MC-MOT pipeline. This challenge is compounded by factors such as varying illumination, diverse walking patterns, and trajectory occlusions. In recent years, graph neural networks (GNNs) have emerged as promising tools for enhancing data association. However, prevalent graph-based MC-MOT methods often rely on computationally inefficient min-cost flow approaches for cross-camera association, with static graph structures that lack adaptability to new detections. Moreover, these methods typically process cameras in pairs, leading to localized solutions rather than a holistic global approach. To address these limitations, we propose a two-stage lightweight crosscamera tracker designed to achieve a global solution efficiently. Our approach prioritizes the quality of local tracklets, enhancing them through supervised learning on multi-source datasets using the DeepSort model. For multi-camera association, we leverage the dynamic connectivity of Message Passing Graph Neural Networks (MPGNNs) to jointly learn features and similarities previously untapped in this domain. Our proposed model significantly improves detection accuracy and feature extraction, outperforming current MC-MOT algorithms on cross-camera benchmarks. This advancement marks a notable step forward in the field, offering more precise tracking capabilities and demonstrating the potential of integrating state-of-theart techniques for enhanced performance in complex tracking scenarios.

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A robust attribute-aware and real-time multi-target multi-camera tracking system using multi-scale enriched features and hierarchical clustering

Cited by 4 publications

References 34 publications

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Tree-based indexing technique for efficient and real-time label retrieval in the object tracking system

Learning to Track With Dynamic Message Passing Neural Network for Multi-Camera Multi-Object Tracking

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