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Multi-Object Multi-Camera Tracking Based on Deep Learning for Intelligent Transportation: A Review.

Lunlin Fei^1,2, Bing Han³

¹School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.

Sensors (Basel, Switzerland)

|April 28, 2023

Summary

This summary is machine-generated.

This review covers deep learning for multi-object multi-camera tracking (MOMCT) in intelligent transportation. It details object detectors, analyzes deep learning methods, and discusses datasets, metrics, and future challenges.

Keywords:

deep neural network intelligent transportation multi-object multi-camera tracking object detector

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Area of Science:

Computer Vision
Artificial Intelligence
Intelligent Transportation Systems

Background:

Multi-Object Multi-Camera Tracking (MOMCT) is crucial for applications like intelligent transportation, public safety, and autonomous driving.
Recent technological advancements have spurred significant research and development in MOMCT.
Keeping up with the latest MOMCT research is vital for advancing intelligent transportation.

Purpose of the Study:

To provide a comprehensive review of deep learning-based MOMCT specifically for intelligent transportation applications.
To consolidate current research, identify key challenges, and suggest future research directions in the field.
To aid researchers in understanding the state-of-the-art and emerging trends in MOMCT.

Main Methods:

Detailed introduction of primary object detection methods used in MOMCT.
In-depth analysis and visual evaluation of advanced deep learning-based MOMCT techniques.
Summary and comparison of popular benchmark datasets and evaluation metrics for quantitative analysis.

Main Results:

Identification of leading object detection algorithms relevant to MOMCT.
Evaluation of various deep learning approaches for multi-object multi-camera tracking.
Compilation of standard datasets and metrics for performance benchmarking.

Conclusions:

Deep learning has significantly advanced MOMCT capabilities for intelligent transportation.
Current challenges in MOMCT include data heterogeneity, real-time processing, and robust tracking in complex scenarios.
Future research should focus on improving model generalization, efficiency, and addressing ethical considerations for widespread deployment.