Person Detection in Thermal Videos Using YOLO

Ivašić-Kos, Marina; Krišto, Mate; Pobar, Miran

doi:10.1007/978-3-030-29513-4_18

Cited by 11 publications

(9 citation statements)

References 17 publications

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“…Fig. 14 presents the precision-recall curve for the Person class of the baseline YOLO model bY, that was not trained on thermal images [24], and the same curve for the model tY that was additionally trained on the thermal images from our dataset for the class Person. The plots are computed on the whole test set.…”

Section: Resultsmentioning

confidence: 99%

“…They integrate a hardwired adaptive Boolean-map-based saliency (ABMS) kernel with the YOLO detector, to generate a saliency feature map that boosts the pedestrian from the background based on the particular season. In [23,24] YOLO detector was trained on a thermal image dataset for person detection. This paper greatly extends the scope of that work by analyzing different weather conditions separately, by testing on other datasets and including possibly confusing objects such as animals in the test.…”

Section: Related Workmentioning

confidence: 99%

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Thermal Object Detection in Difficult Weather Conditions Using YOLO

2020

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Thermal Object Detection in Difficult Weather Conditions Using YOLO

2020

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“…Our approach is quite general, as it can handle a variety of UAV detection situations [35,36]. Empirical performance evaluations established the advantages of the proposed method over other state-of-the-art algorithms, as detailed below.…”

Section: Tracking On the Current Image Planementioning

confidence: 91%

Toward More Robust and Real-Time Unmanned Aerial Vehicle Detection and Tracking via Cross-Scale Feature Aggregation Based on the Center Keypoint

et al. 2021

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Unmanned aerial vehicles (UAVs) play an essential role in various applications, such as transportation and intelligent environmental sensing. However, due to camera motion and complex environments, it can be difficult to recognize the UAV from its surroundings thus, traditional methods often miss detection of UAVs and generate false alarms. To address these issues, we propose a novel method for detecting and tracking UAVs. First, a cross-scale feature aggregation CenterNet (CFACN) is constructed to recognize the UAVs. CFACN is a free anchor-based center point estimation method that can effectively decrease the false alarm rate, the misdetection of small targets, and computational complexity. Secondly, the region of interest-scale-crop-resize (RSCR) method is utilized to merge CFACN and region-of-interest (ROI) CFACN (ROI-CFACN) further, in order to improve the accuracy at a lower computational cost. Finally, the Kalman filter is adopted to track the UAV. The effectiveness of our method is validated using a collected UAV dataset. The experimental results demonstrate that our methods can achieve higher accuracy with lower computational cost, being superior to BiFPN, CenterNet, YoLo, and their variants on the same dataset.

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“…Data fusion of environmental sensors were confirmed to validate individual sensors for improved performance [7]. Some previous studies on estimating the number of people present in buildings utilized red, green, and blue (RGB) camera thermal arrays [8][9][10][11][12][13][14][15][16][17][18]. A head detection method with an accuracy ranging from 90 to 95% for different scenarios was proposed by Oosterhout and coauthors based on range data from stereo cameras for counting people [18].…”

Section: Related Workmentioning

confidence: 99%

Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy

et al. 2021

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The control of thermostats of a heating, ventilation, and air-conditioning (HVAC) system installed in commercial and residential buildings remains a pertinent problem in building energy efficiency and thermal comfort research. The ability to determine the number of people at a particular time in an area is imperative for energy efficiency in order to condition only occupied regions and thermally deficient regions. In this study of the best features comparison for detecting the number of people in an area, feature extraction techniques including wavelet scattering, wavelet decomposition, grey-level co-occurrence matrix (GLCM) and feature maps convolution neural network (CNN) layers were explored using thermal camera imagery. Specifically, the pretrained CNN networks explored are the deep residual (Resnet-50) and visual geometry group (VGG-16) networks. The discriminating potential of Haar, Daubechies and Symlets wavelet statistics on different distributions of data were investigated. The performance of VGG-16 and ResNet-50 in an end-to-end manner utilizing transfer learning approach was investigated. Experimental results showed the classification and regression trees (CART) model trained on only GLCM and Haar wavelet statistic features, individually achieved accuracies of approximately 80% and 84%, respectively, in the detection problem. Moreover, k-nearest neighbors (KNN) trained on the combined features of GLCM and Haar wavelet statistics achieved an accuracy of approximately 86%. In addition, the performance accuracy of the multi classification support vector machine (SVM) trained on deep features obtained from layers of pretrained ResNet-50 and VGG-16 was between 96% and 97%. Furthermore, ResNet-50 transfer learning outperformed the VGG-16 transfer learning model for occupancy detection using thermal imagery. Overall, the SVM model trained on features extracted from wavelet scattering emerged as the best performing classifier with an accuracy of 100%. A principal component analysis (PCA) on the wavelet scattering features proved that the first twenty (20) principal components achieved a similar accuracy level instead of training on the whole feature set to reduce the execution time. The occupancy detection models can be integrated into HVAC control systems for energy efficiency and security systems, and aid in the distribution of resources to people in an area.

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Person Detection in Thermal Videos Using YOLO

Cited by 11 publications

References 17 publications

Thermal Object Detection in Difficult Weather Conditions Using YOLO

Thermal Object Detection in Difficult Weather Conditions Using YOLO

Toward More Robust and Real-Time Unmanned Aerial Vehicle Detection and Tracking via Cross-Scale Feature Aggregation Based on the Center Keypoint

Thermal Imagery Feature Extraction Techniques and the Effects on Machine Learning Models for Smart HVAC Efficiency in Building Energy

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