Bird's Eye View Transformation Technique in Photogrammetric Problem of Object Size Measuring at Low-altitude Photography

Kholopov, Ivan S.

doi:10.2991/aime-17.2017.52

Cited by 7 publications

(5 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…RPM takes as input frontal view, and then it applies the homography, and establishes a top-down view of the captured scene. Figure 5 shows the captured image from a perspective to top-down view (bird’’s-eye view), where the dimensions in the image have a real interconnection with real-world dimensions [ 49 ]. The transformation matrix (TM) is computed using a function in OpenCV Library, which is defined as “ get Perspective Transform ”; see Eqs.…”

Section: Methodsmentioning

confidence: 99%

Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19

Saponara

Elhanashi

Zheng

2022

J Real-Time Image Proc

View full text Add to dashboard Cite

COVID-19 is a virus, which is transmitted through small droplets during speech, sneezing, coughing, and mostly by inhalation between individuals in close contact. The pandemic is still ongoing and causes people to have an acute respiratory infection which has resulted in many deaths. The risks of COVID-19 spread can be eliminated by avoiding physical contact among people. This research proposes real-time AI platform for people detection, and social distancing classification of individuals based on thermal camera. YOLOv4-tiny is proposed in this research for object detection. It is a simple neural network architecture, which makes it suitable for low-cost embedded devices. The proposed model is a better option compared to other approaches for real-time detection. An algorithm is also implemented to monitor social distancing using a bird’s-eye perspective. The proposed approach is applied to videos acquired through thermal cameras for people detection, social distancing classification, and at the same time measuring the skin temperature for the individuals. To tune up the proposed model for individual detection, the training stage is carried out by thermal images with various indoor and outdoor environments. The final prototype algorithm has been deployed in a low-cost Nvidia Jetson devices (Xavier and Jetson Nano) which are composed of fixed camera. The proposed approach is suitable for a surveillance system within sustainable smart cities for people detection, social distancing classification, and body temperature measurement. This will help the authorities to visualize the fulfillment of the individuals with social distancing and simultaneously monitoring their skin temperature.

show abstract

Section: Methodsmentioning

confidence: 99%

Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19

Saponara

Elhanashi

Zheng

2022

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…In this work, we assume the homography matrix H and the image-to-real distance scale α s to be known for simplicity; however, they can be obtained by GPS and accelerometers [38][39][40], determined by calibration [41,42], inferred from the computed poses [26,27], or estimated by a four-point perspective transformation [43].…”

Section: Top-view Transformationmentioning

confidence: 99%

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Al-Sa'D

Kıranyaz

Ahmad

et al. 2022

Sensors

View full text Add to dashboard Cite

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.

show abstract

“…Therefore, since the resolution and the number of pixels in the image are known, the application can calculate the number of pixels for a 6-ft. distance in an arbitrary image. Figure 2 shows the original image captured from a perspective to the vertical view of bird's eye, where the dimensions in the picture have a linear relationship with real dimensions [30]. The relationship between pixel of (x, y) in the bird's eye picture and pixel of (u, v) in the original picture is defined as follows:…”

Section: Physical Distance Detectionmentioning

confidence: 99%

“…A perspective image transformation to a bird's eye image [30] With mask With mask Incorrect+With mask Without mask Where x=x′/w′ and y=y′/w′. For the transformation matrix, the OpenCV library in Python was used [33].…”

Section: Physical Distance Detectionmentioning

confidence: 99%

An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic

et al. 2021

View full text Add to dashboard Cite

The COVID-19 pandemic has caused many shutdowns in different industries around the world. Sectors such as infrastructure construction and maintenance projects have not been suspended due to their significant effect on people's routine life. In such projects, workers work close together that makes a high risk of infection. The World Health Organization recommends wearing a face mask and practicing physical distancing to mitigate the virus's spread. This paper developed a computer vision system to automatically detect the violation of face mask wearing and physical distancing among construction workers to assure their safety on infrastructure projects during the pandemic. For the face mask detection, the paper collected and annotated 1,000 images, including different types of face mask wearing, and added them to a pre-existing face mask dataset to develop a dataset of 1,853 images. Then trained and tested multiple TensorFlow state-of-the-art object detection models on the face mask dataset and chose the Faster R-CNN Inception ResNet V2 network that yielded the accuracy of 99.8%. For physical distance detection, the paper employed the Faster R-CNN Inception V2 to detect people. A transformation matrix was used to eliminate the camera angle's effect on the object distances on the image. The Euclidian distance used the pixels of the transformed image to compute the actual distance between people. A threshold of six feet was considered to capture physical distance violation. The paper also used transfer learning for training the model. The final model was applied on several videos of road maintenance projects in Houston, TX, that effectively detected the face mask and physical distance. We recommend that construction owners use the proposed system to enhance construction workers' safety in the pandemic situation.

show abstract

Bird's Eye View Transformation Technique in Photogrammetric Problem of Object Size Measuring at Low-altitude Photography

Cited by 7 publications

References 21 publications

Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19

Developing a real-time social distancing detection system based on YOLOv4-tiny and bird-eye view for COVID-19

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

An Automatic System to Monitor the Physical Distance and Face Mask Wearing of Construction Workers in COVID-19 Pandemic

Contact Info

Product

Resources

About