Michele Zanin

A computer vision system for the detection and classification of vehicles at urban road intersections

The paper presents a real-time vision system to compute traffic parameters by analyzing monocular image sequences coming from pole-mounted video cameras at urban crossroads. The system uses a combination of segmentation and motion information to localize and track moving objects on the road plane, utilizing a robust background updating, and a feature-based tracking method. It is able to describe the path of each detected vehicle, to estimate its speed and to classify it into seven categories. The classification task relies on a model-based matching technique refined by a feature-based one for distinguishing between classes having similar models, like bicycles and motorcycles. The system is flexible with respect to the intersection geometry and the camera position. Experimental results demonstrate robust, real-time vehicle detection, tracking and classification over several hours of videos taken under different illumination conditions. The system is presently under trial in Trento, a 100,000-people town in northern Italy.

Intelligent extended floating car data collection

The elaboration of data collected by vehicles moving on road network is relevant for traffic management and for private service providers, which can bundle updated traffic information with navigation services. Floating data, in its extended acceptation, contains not only time and location provided by a positioning system, but also information coming from various vehicle sensors. In this paper we describe our extended data collection system, in which vehicles are able to collect data about their local environment, namely the presence of roadworks and traffic slowdowns, by analyzing visual data taken by a looking forward camera and data from the on-board Electronic Control Unit. Upon detection of such events, a packet is set up containing time, position, vehicle data, results of on-board elaboration, one or more images of the road ahead and an estimation of the local traffic level. Otherwise, the transmitted packet containing only the minimal data, making its size adaptive to the environment surrounding the vehicle.

A kalman filter based background updating algorithm robust to sharp illumination changes

A novel algorithm, based on Kalman filtering is presented for updating the background image within video sequences. Unlike existing implementations of the Kalman filter for this task, our algorithm is able to deal with both gradual and sudden global illumination changes. The basic idea is to measure global illumination change and to use it as an external control of the filter. This allows the system to better fit the assumptions about the process to be modeled. Moreover, we propose methods to estimate measurement noise variance and to deal with the problem of saturated pixels, to improve the accuracy and robustness of the algorithm. The algorithm has been successfully tested in a traffic surveillance task by comparing it to a background updating algorithm, based on Kalman filtering, taken from literature.

Unified stereovision for ground, road, and obstacle detection

This paper presents a method for road detection and obstacle detection entirely based on stereovision. The ground plane is estimated online by least square fitting of disparity data. This operation allows deleting road features for obstacle detection, estimating directly camera roll and pitch, and deriving some clues on road-surface image regions. A model-based algorithm employing only disparity information is demonstrated to be able to segment the whole road surface without knowledge of infrastructures and features like lane markings. This helps navigation in suburban and country-road environments, and recovery from critical failure of lane-markings trackers.

Switching Models for Vision-based On-Board Road Detection

This paper illustrates a model-based algorithm for road segmentation to be integrated into an in-vehicle vision system. Specifically, it describes the work-in-progress to introduce a novel on-line switching model strategy in such an algorithm. Some preliminary results are presented, along with preliminary quantitative assessments of the improvements brought by the model-switching control.

An efficient vehicle queue detection system based on image processing

This paper describes a method for the real-time measurement of vehicle queue parameters in a video-based traffic monitoring experimental system. The method proposed here is based on vehicle presence detection and movement analysis in video sequences acquired by a stationary camera. Queues are detected and characterized by a severity index. Intensive experiments show the robustness of the method under varying illumination and weather conditions. The system is presently undergoing an on-field testing phase in a double ways road near Trento, Italy, where queues frequently occur.

Visual-inertial tracking on Android for Augmented Reality applications

Augmented Reality (AR) aims to enhance a persons vision of the real world with useful information about the surrounding environment. Amongst all the possible applications, AR systems can be very useful as visualization tools for structural and environmental monitoring. While the large majority of AR systems run on a laptop or on a head-mounted device, the advent of smartphones have created new opportunities. One of the most important functionality of an AR system is the ability of the device to self localize. This can be achieved through visual odometry, a very challenging task for smartphone. Indeed, on most of the available smartphone AR applications, self localization is achieved through GPS and/or inertial sensors. Hence, developing an AR system on a mobile phone also poses new challenges due to the limited amount of computational resources. In this paper we describe the development of a egomotion estimation algorithm for an Android smartphone. We also present an approach based on an Extended Kalman Filter for improving localization accuracy integrating the information from inertial sensors. The implemented solution achieves a localization accuracy comparable to the PC implementation while running on an Android device.

Spatial and Temporal Attractiveness Analysis Through Geo-Referenced Photo Alignment

This paper presents a system to create a spatiotemporal attractiveness GIS layer for mountainous areas brought about by the implementation of novel image processing and pattern matching algorithms. We utilize the freely available Digital Terrain Model of the planet provided by NASA [1] to generate a three-dimensional synthetic model around a viewers location. Using an array of image processing algorithms we then align photographs to this model. We will demonstrate the accuracy of the resulting system through the overlaying of geo-referenced content, such as mountain names and then we will suggest ways in which visitors/photographers can exploit the results of this research, such as suggesting temporally appropriate photo-hotspots close to their current location.

Localization of ahead vehicles with on-board stereo cameras

This paper introduces a vision based algorithm that detects and localizes ahead vehicles elaborating images taken by a stereo camera installed on an intelligent vehicle. The algorithm is based on the analysis of stereo images, estimating the ground plane by least square fitting of disparity data, and segmenting the obstacles by a rule based split/merge strategy. Quantitative experiments on complex real world sequences validate the approach. The method is demonstrated to operate in real-time.

Augmented reality: Fusing the real and synthetic worlds

Augmented Reality (AR) offers a means to inject virtual information into real scenes. In the past few years, AR has been receiving greater attention thanks to considerable advancements in the hardware of consumer-level portable devices. In this paper, we illustrate how mobile AR can be exploited to intuitively visualize, and moreover generate new, geo-data. We explore these two concepts through i) the visualization and interaction modality of geo-data as AR layers and ii) the exploitation of mobile devices as opportunistic sensors for generating information relating to a users immediate surroundings.