On-Site Global Calibration of Mobile Vision Measurement System Based on Virtual Omnidirectional Camera Model

The mobile vision measurement system (MVMS) is widely used for location and attitude measurement in aircraft takeoff and landing, and its on-site global calibration is crucial to obtaining high-accuracy measurement aimed at obtaining the transformation relationship between the MVMS coordinate system and the local-tangent-plane coordinate system. In this paper, several new ideas are proposed to realize the global calibration of the MVMS effectively. First, the MVMS is regarded as azimuth and pitch measurement equipment with a virtual single image plane at focal length 1. Second, a new virtual omnidirectional camera model constructed by three mutual orthogonal image planes is put forward, which effectively resolves the problem of global calibration error magnification when the angle between the virtual single image plane and view axis of the system becomes small. Meanwhile, an expanded factorial linear method is proposed to solve the global calibration equations, which effectively restrains the influence of calibration data error. Experimental results with synthetic data verify the validity of the proposed method.

Drowsiness Detection using Android Application and Mobile Vision Face API

ABSTRACT Absence of forbearance among drivers, fatigue and irresponsible behaviour among drivers result in countless fatal crashes and road traffic injuries. Driver drowsiness is a highly problematic issue which impairs judgment and decision making among drivers resulting in fatal motor crashes. This paper describes a simple drowsiness detection approach for a smartphone with Android application using Android Studio 3.6.1 and Mobile Vision API for drowsiness detection before and while driving. Physiological analysis and a quick facial analysis were performed to check drowsiness before the driver starts driving. The smartphone camera was used for analysing the heart rate by tracking colour changes due to blood flow on the fingertip. Facial analysis was undertaken by Google Vision API which determined the head position, blinking duration and yawning frequency through the eye opening and mouth opening probabilities. The heart rate, blinking duration, yawning frequency and speeding were used as indicators for drowsiness. The facial analysis was repeated with speeding data while driving with results analysed each one minute. A performance accuracy of the combined results with speeding detection proved to be around 93.3%. Additional keywords: Drowsiness detection; Facial analysis; Heartrate; Mobile Vision API; Physiological analysis.

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.