Accuracy And Error Study Of Horizontal And Vertical Measurements With Single View Metrology For Road Surveying

High quality digital image can be produced and stored with cost effective embedded system, thanks to advancement of low power digital camera and hardware accelerated high definition video image compression System-on- Chip. Image recorded with these multi-megapixel digital cameras allowed the world to be digitized more accurately (compared with conventional VGA camera with low resolution) and hence enable the use of single image as the metrology tool. Using the single view geometry techniques (planar homography, vanishing points and vanishing lines) widely accepted by the community, the suitability of applying these techniques with error reduced for road surveying is studied and reported in this work

The Visual Social Distancing Problem

One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a possible threat given the scene context. All of this, complying with privacy policies and making the measurement acceptable. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of the related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this manuscript and they are listed by alphabetical order. Under submissio

Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.Comment: Accepted for publication at German Conference on Pattern Recognition (GCPR) 2017. This research was supported by German Research Foundation DFG within Priority Research Programme 1894 "Volunteered Geographic Information: Interpretation, Visualisation and Social Computing

Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods

A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models' residual variances - however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth

INTELLIGENT ADS

Advertisements are essential in today’s industry. They play an important role in connecting the producer and consumers. However, today’s advertisements are general advertisements. The advertisements displayed do not focus on certain group of people. This will cause a waste in the billboard energy and the advertiser’s investment. Adult and children will have to look at the same advertisement regardless of its contents and appropriateness. The aim of this project is to design a system that could measure human height and be able to distinguish between adults and kids. Studies had been conducted on several methods to measure human height. It’s working principle and limitations had been discovered and through that, the best method to measure human height was developed. A prototype consists of a laptop with an installed Matlab software and a webcam had to be set up. This project has been successfully completed. The system designed has achieved the objectives set for this project