Video-based Navigation Research Articles

Стабилизация видеоизображения в режиме реального времени с использованием MEMS-датчиков

This article describes our ongoing research on real-time digital video stabilization using MEMS-sensors. The authors propose to use the described method for stabilizing the video that is transmitted to the mobile robot operator who controls the vehicle remotely, as well as increasing the precision of video-based navigation for subminiature autonomous models. The article describes the general mathematical models needed to implement the video stabilization module based on the MEMS sensors readings. These models includes the camera motion model, frame transformation model and rolling-shutter model. The existing approaches to stabilization using sensors data were analyzed and considered from the point of view of the application in a real-time mode. This article considers the main problems that came up during the experiments that were not resolved in the previous research papers. Such problems include: calibration of the camera and sensors, synchronization of the camera and sensors, increasing the accuracy of determining the camera position from sensors data. The authors offer possible solutions to these problems that would help improve quality of the work of existing algorithms, such as a system for parallel synchronized recording of video and sensor data based on the Android operating system. As the main result, the authors represent a framework for implementing video stabilization algorithms based on MEMS sensors readings.

SA-FEMIP: A Self-Adaptive Features Extractor and Matcher IP-Core Based on Partially Reconfigurable FPGAs for Space Applications

Video-based navigation (VBN) is increasingly used in space applications to enable autonomous entry, descent, and landing of aircrafts. VBN algorithms require real-time performances and high computational capabilities, especially to perform features extraction and matching (FEM). In this context, field-programmable gate arrays (FPGAs) can be employed as efficient hardware accelerators. This paper proposes an improved FPGA-based FEM module. Online self-adaptation of the parameters of both the image noise filter and the features extraction algorithm is adopted to improve the algorithm robustness. Experimental results demonstrate the effectiveness of the proposed self-adaptive module. It introduces a marginal resource overhead and no timing performance degradation when compared with the reference state-of-the-art architecture.

A Sensor Fusion Framework Using Multiple Particle Filters for Video-Based Navigation

This paper presents a sensor-fusion framework for video-based navigation. Video-based navigation offers the advantages over existing approaches. With this type of navigation, road signs are directly superimposed onto the video of the road scene, as opposed to those superimposed onto a 2-D map, as is the case with conventional navigation systems. Drivers can then follow the virtual signs in the video to travel to the destination. The challenges of video-based navigation require the use of multiple sensors. The sensor-fusion framework that we propose has two major components: (1) a computer vision module for accurately detecting and tracking the road by using partition sampling and auxiliary variables and (2) a sensor-fusion module using multiple particle filters to integrate vision, Global Positioning Systems (GPSs), and Geographical Information Systems (GISs). GPS and GIS provide prior knowledge about the road for the vision module, and the vision module, in turn, corrects GPS errors.

Methods to Detect Road Features for Video-Based In-Vehicle Navigation Systems

Understanding road features such as position and color of lane markings in a live video captured from a moving vehicle is essential in building video-based car navigation systems. In this article, the authors present a framework to detect road features in 2 difficult situations: (a) ambiguous road surface conditions (i.e., damaged roads and occluded lane markings caused by the presence of other vehicles on the road) and (b) poor illumination conditions (e.g., backlight, during sunset). Furthermore, to understand the lane number that a driver is driving on, the authors present a Bayesian network (BN) model, which is necessary to support more sophisticated navigation services for drivers such as recommending lane change at an appropriate time before turning left or right at the next intersection. In the proposed BN approach, evidence from (1) a computer vision engine (e.g., lane-color detection) and (2) a navigation database (e.g., the total number of lanes) was fused to more accurately decide the lane number. Extensive simulation results indicated that the proposed methods are both robust and effective in detecting road features for a video-based car navigation system.

Robust Road Modeling and Tracking Using Condensation

In this paper, we present a robust road detection and tracking method based on a condensation particle filter for real-time video-based navigation applications. The image is divided into horizontal strips, and vanishing point (VP) detection is performed on each image strip. We propose a method for estimating the density of road boundary line segments in the image so that VP detection in an image strip takes into account the detection results in the neighboring image strips. This use of contextual information for VP detection leads to more accurate detection results. The estimated road parameters are then used to initialize the condensation tracker. Experiments using real road videos demonstrate the robustness of our method to difficult road conditions due to the presence of partial occlusion, shadows, and road signs.

Real-Time Apartment Building Detection and Tracking with AdaBoost Procedure and Motion-Adjusted Tracker

In this letter, we propose a novel approach to detecting and tracking apartment buildings for the development of a video-based navigation system that provides augmented reality representation of guidance information on live video sequences. For this, we propose a building detector and tracker. The detector is based on the AdaBoost classifier followed by hierarchical clustering. The classifier uses modified Haar-like features as the primitives. The tracker is a motion-adjusted tracker based on pyramid implementation of the Lukas-Kanade tracker, which periodically confirms and consistently adjusts the tracking region. Experiments show that the proposed approach yields robust and reliable results and is far superior to conventional approaches.