Noninvasive Passenger Detection Comparison Using Thermal Imager and IP Cameras

Kuchár, Pavol; Pirník, Rastislav; Tichý, Tomáš; Rástočný, Karol; Skuba, Michal; Tettamanti, Tamás

doi:10.3390/su132212928

Cited by 13 publications

(7 citation statements)

References 26 publications

(27 reference statements)

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“…A practical and accurate method to count individuals in a specific area can be to use a combination of visual and infrared devices. As demonstrated in [116], where authors experimented with a thermal imager in a noninvasive manner, such a system can only be utilized in conjunction with the detection of individuals inside vehicles. The authors tried with closed windows but had unsatisfactory results so they opened automobile windows at various levels to test the accuracy of their suggested technique.…”

Section: Counting Passengers At Public Transport Stationsmentioning

confidence: 99%

“…This prediction map is interpreted in the way that each cell can predict a fixed number of bounding boxes. YOLO can achieve fast frame rates [116,135].…”

Section: Mathematical Models 61 You Only Look Oncementioning

confidence: 99%

“…The loss function indicates the performance of the model. YOLO loss function is defined as equation [116,135]:…”

Section: Mathematical Models 61 You Only Look Oncementioning

confidence: 99%

See 2 more Smart Citations

Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

et al. 2023

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Passenger detection and occupancy estimation are vital tasks in many fields. The existing literature emphasises that the increasing demand for such systems will continue to grow. This paper reviews the existing literature specializing in the field of transportation safety and efficiency concerning occupancy estimation in vehicles and passenger detection at public transport stations. A comparison between different approaches to passenger estimation is presented. Discussion on the advantages and disadvantages is highlighted. Hence, this paper provides an analysis of 146 papers on the current state of the field. This review paper concludes that invasive methods provide high accuracy with relatively cheap implementation, while noninvasive systems do not violate passenger privacy but lack state-of-the-art accuracy. Future work will include a systematic literature review and a comparative analysis of systems considering the existing window tinting and solar windshields heavily blocking certain parts of the electromagnetic spectrum. Moreover, future work will investigate the critical challenges of noninvasive passenger estimation in different types of vehicles: trucks, buses, or even motorcycles.

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Section: Counting Passengers At Public Transport Stationsmentioning

confidence: 99%

“…This prediction map is interpreted in the way that each cell can predict a fixed number of bounding boxes. YOLO can achieve fast frame rates [116,135].…”

Section: Mathematical Models 61 You Only Look Oncementioning

confidence: 99%

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Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

et al. 2023

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“…Nasir et al [ 15 ] developed a methodology for estimating the number of passengers on a bus, which involves processing on-board bus images using several image processing techniques including colour conversion, image segmentation, and the removal of noise. Likewise, some authors [ 16 , 17 , 18 ] have used image processing for estimating the passenger count by attempting image segmentation on different colour conversions or with the help of depth/thermal cameras. Other passenger counting techniques have been proposed that harness machine learning (ML) and DL algorithms.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study

Pronello,

Garzón Ruiz

2023

Sensors

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Automatic passenger counting (APC) systems in public transport are useful in collecting information that can help improve the efficiency of transport networks. Focusing on video-based passenger counting, the aim of this study was to evaluate and compare an existing APC system, claimed by its manufacturer to be highly accurate (98%), with a newly developed low-cost APC system operating under the same real-world conditions. For this comparison, a low-cost APC system using a Raspberry Pi with a camera and a YOLOv5 object detection algorithm was developed, and an in-field experiment was performed in collaboration with the public transport companies operating in the cities of Turin and Asti in Italy. The experiment shows that the low-cost system was able to achieve an accuracy of 72.27% and 74.59%, respectively, for boarding and alighting, while the tested commercial APC system had an accuracy, respectively, of 53.11% and 55.29%. These findings suggest that current APC systems might not meet expectations under real-world conditions, while low-cost systems could potentially perform at the same level of accuracy or even better than very expensive commercial systems.

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“…Therefore, the introduction of the CBAM module in the neural network to weight the features of the target region. It can enable better localization over the features to be detected and also improve the generalization performance of the network while not introducing too many parametric quantities [ 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

A New Deep Model for Detecting Multiple Moving Targets in Real Traffic Scenarios: Machine Vision-Based Vehicles

Xiong

Liu

et al. 2022

Sensors

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When performing multiple target detection, it is difficult to detect small and occluded targets in complex traffic scenes. To this end, an improved YOLOv4 detection method is proposed in this work. Firstly, the network structure of the original YOLOv4 is adjusted, and the 4× down-sampling feature map of the backbone network is introduced into the neck network of the YOLOv4 model to splice the feature map with 8× down-sampling to form a four-scale detection structure, which enhances the fusion of deep and shallow semantics information of the feature map to improve the detection accuracy of small targets. Then, the convolutional block attention module (CBAM) is added to the model neck network to enhance the learning ability for features in space and on channels. Lastly, the detection rate of the occluded target is improved by using the soft non-maximum suppression (Soft-NMS) algorithm based on the distance intersection over union (DIoU) to avoid deleting the bounding boxes. On the KITTI dataset, experimental evaluation is performed and the analysis results demonstrate that the proposed detection model can effectively improve the multiple target detection accuracy, and the mean average accuracy (mAP) of the improved YOLOv4 model reaches 81.23%, which is 3.18% higher than the original YOLOv4; and the computation speed of the proposed model reaches 47.32 FPS. Compared with existing popular detection models, the proposed model produces higher detection accuracy and computation speed.

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Noninvasive Passenger Detection Comparison Using Thermal Imager and IP Cameras

Cited by 13 publications

References 26 publications

Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

Evaluating the Performance of Video-Based Automated Passenger Counting Systems in Real-World Conditions: A Comparative Study

A New Deep Model for Detecting Multiple Moving Targets in Real Traffic Scenarios: Machine Vision-Based Vehicles

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