Image Motion Extraction of Structures Using Computer Vision Techniques: A Comparative Study

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor's taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor's categorization enables us to compare the effectiveness of each sensor's system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

Localization of Stereovision for Measuring In-Crash Toeboard Deformation

This paper presents a technique to localize a stereo camera for in-crash toeboard deformation measurement. The proposed technique designed a sensor suite to install not only the stereo camera but also initial measurement units (IMUs) and a camera for localizing purpose. The pose of the stereo camera is recursively estimated using the measurement of IMUs and the localization camera through an extended Kalman filter. The performance of the proposed approach was first investigated in a stepwise manner and then tested in controlled environments including an actual vehicle crash test, which had successfully resulted in measuring the toeboard deformation during a crash. With the oscillation motion in the occurrence of the crash captured, the deformation of the toeboard measured by stereo cameras can be described in a fixed coordinate system.

Moving Object Tracking Based on Sparse Optical Flow with Moving Window and Target Estimator

Moving object detection and tracking are technologies applied to wide research fields including traffic monitoring and recognition of workers in surrounding heavy equipment environments. However, the conventional moving object detection methods have faced many problems such as much computing time, image noises, and disappearance of targets due to obstacles. In this paper, we introduce a new moving object detection and tracking algorithm based on the sparse optical flow for reducing computing time, removing noises and estimating the target efficiently. The developed algorithm maintains a variety of corner features with refreshed corner features, and the moving window detector is proposed to determine the feature points for tracking, based on the location history of the points. The performance of detecting moving objects is greatly improved through the moving window detector and the continuous target estimation. The memory-based estimator provides the capability to recall the location of corner features for a period of time, and it has an effect of tracking targets obscured by obstacles. The suggested approach was applied to real environments including various illumination (indoor and outdoor) conditions, a number of moving objects and obstacles, and the performance was evaluated on an embedded board (Raspberry pi4). The experimental results show that the proposed method maintains a high FPS (frame per seconds) and improves the accuracy performance, compared with the conventional optical flow methods and vision approaches such as Haar-like and Hog methods.