An Assessment of Object Detection in Thermal (Infrared) Image Processing

Kamaleswari, P.; Krishnaraj, N.

doi:10.1109/icais56108.2023.10073709

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2024

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Cited by 3 publications

References 15 publications

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A Deep Assessment of Thermal Image-Base Object Detection for a Wide Range of Applications

Pandurangan,

Nagappan

2024

2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet O

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A Deep Assessment of Thermal Image-Base Object Detection for a Wide Range of Applications

Pandurangan,

Nagappan

2024

2024 2nd International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet O

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Review on Various Techniques for Wildlife Monitoring and Alerting Systems

Bandaru,

Basa,

Raghavendra

et al. 2024

2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS)

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A Computer vision based system for human detection and automatic people counting

Curiel,

Guerrero,

Gómez

et al. 2024

TESEA

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Occupancy control is a fundamental aspect of managing spaces and services effectively. It aims to ensure safety, compliance with regulations, emergency preparedness, and overall satisfaction for individuals and businesses. To align with the described need, this paper presents a computer vision based system for automatic people counting in gates. The system is divided in five stages: video capture, motion analysis, human detection, human tracking and people counting. An RGB camera captures the top-view image of the gate and analyze the change or movement in the objects in scene. When motion is detected, the frame is sent to the object detector, which is a convolutional neural network. Then, a tracking algorithm analyzes the movement patterns of people. According to the route, it is determined whether the person arrives or leaves and the count is updated. Two test scenarios are analyzed: the entry of a public bus and a building gate. The people detection module is tested, showing a mAP of 95.2% and a mean IoU (50%) of 55.9%. Also, the counting is tested showing an average precision of 96.8%, a recall of 92% and an F1-Score of 94.3%. Finally, the system performance is evaluated, showing an average processing time of 34.2 ms and 29.2 FPS.

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An Assessment of Object Detection in Thermal (Infrared) Image Processing

Cited by 3 publications

References 15 publications

A Deep Assessment of Thermal Image-Base Object Detection for a Wide Range of Applications

A Deep Assessment of Thermal Image-Base Object Detection for a Wide Range of Applications

Review on Various Techniques for Wildlife Monitoring and Alerting Systems

A Computer vision based system for human detection and automatic people counting

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