Arcangelo Distante

A visual approach for driver inattention detection

Monitoring driver fatigue, inattention, and lack of sleep is very important in preventing motor vehicles accidents. A visual system for automatic driver vigilance has to address two fundamental problems. First of all, it has to analyze the sequence of images and detect if the driver has his eyes open or closed, and then it has to evaluate the temporal occurrence of eyes open to estimate the drivers visual attention level. In this paper we propose a visual approach that solves both problems. A neural classifier is applied to recognize the eyes in the image, selecting two candidate regions that might contain the eyes by using iris geometrical information and symmetry. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Several experiments were carried out on images of subjects with different eye colors, some of them wearing glasses, in different light conditions. Tests show robustness with respect to situations such as eyes partially occluded, head rotation and so on. In particular, when applied to images where people have eyes closed the proposed algorithm correctly reveals the absence of eyes. Next, the analysis of the eye occurrence in image sequences is carried out with a probabilistic model to recognize anomalous behaviors such as driver inattention or sleepiness. Image sequences acquired in the laboratory and while people were driving a car were used to test the driver behavior analysis and demonstrate the effectiveness of the whole approach.

Moving object segmentation by background subtraction and temporal analysis

In this paper, we address the problem of moving object segmentation using background subtraction. Solving this problem is very important for many applications: visual surveillance of both in outdoor and indoor environments, traffic control, behavior detection during sport activities, and so on. All these applications require as a first step, the detection of moving objects in the observed scene before applying any further technique for object recognition and activity identification. We propose a reliable foreground segmentation algorithm that combines temporal image analysis with a reference background image. We are especially careful of the core problem arising in the analysis of outdoor daylight scenes: continuous variations of lighting conditions that cause unexpected changes in intensities on the background reference image. In this paper, a new approach for background adaptation to changes in illumination is presented. All the pixels in the image, even those covered by foreground objects, are continuously updated in the background model. The experimental results demonstrate the effectiveness of the proposed algorithm when applied in different outdoor and indoor environments.

A new algorithm for ball recognition using circle Hough transform and neural classifier

A large number of methods for circle detection have been studied in the last years for several image processing applications. The context application considered in this work is the soccer game. In the sequences of soccer images it is very important to identify the ball in order to verify the goal event. This domain is a challenging one as a great number of problems have to be faced, such as occlusions, shadows, objects similar to the ball, real-time processing and so on. In this work a visual framework trying to solve the above-stated problems, mainly considering real-time computational aspects, has been developed. The ball detection algorithm has to be very simple in terms of time processing and also has to be efficient in terms of false positive rate. Our framework consists of two sequential steps for solving the ball recognition problem: the first step uses a modified version of the directional circle Hough transform to detect the region of the image that is the best candidate to contain the ball; in the second step a neural classifier is applied on the selected region to confirm if the ball has been properly detected or a false positive has been found. Some tricks like background subtraction and ball tracking have been applied in order to maintain the search of the ball only in limited areas of the image. Different light conditions have been considered as they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light, as the light source is strictly directional, the ball, due to self-shades, appears as a spherical cap; this case has been taken in account and the search of the ball has been modified in order to manage this situation. A large number of experiments have been carried out showing that the proposed method obtains a high detection score.

An algorithm for real time eye detection in face images

The problem of eye detection in face images is very important for a large number of applications ranging from face recognition to gaze tracking. In this paper, we propose a new algorithm for eyes detection that uses iris geometrical information for determining in the whole image the region candidate to contain an eye, and then the symmetry for selecting the couple of eyes. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Different experiments, carried out on images of subjects with different eyes colors, some of them wearing glasses, demonstrate the effectiveness and robustness of the proposed algorithm.

Mobile robot position determination using visual landmarks

This paper is concerned with the problem of determining the position of a mobile vehicle during navigation. In order to achieve this objective a multisensor navigation system for self location of the robot has been developed. By tracking a few known landmarks with a vision module, the system is able to monitor continuously its position and to integrate these estimates with the measures provided by the vehicle odometers. This paper describes in detail the vision module used by the navigation system. >

An Autonomous Mobile Robotic System for Surveillance of Indoor Environments

The development of intelligent surveillance systems is an active research area. In this context, mobile and multi-functional robots are generally adopted as means to reduce the environment structuring and the number of devices needed to cover a given area. Nevertheless, the number of different sensors mounted on the robot, and the number of complex tasks related to exploration, monitoring, and surveillance make the design of the overall system extremely challenging. In this paper, we present our autonomous mobile robot for surveillance of indoor environments. We propose a system able to handle autonomously general-purpose tasks and complex surveillance issues simultaneously. It is shown that the proposed robotic surveillance scheme successfully addresses a number of basic problems related to environment mapping, localization and autonomous navigation, as well as surveillance tasks, like scene processing to detect abandoned or removed objects and people detection and following. The feasibility of the approach is demonstrated through experimental tests using a multisensor platform equipped with a monocular camera, a laser scanner, and an RFID device. Real world applications of the proposed system include surveillance of wide areas (e.g. airports and museums) and buildings, and monitoring of safety equipment.

A visual system for real time detection of goal events during soccer matches

During soccer matches a number of doubtful situations arise that cannot be easily judged by the referee committee. An automatic visual system that checks objectively image sequences would prevent wrong interpretations due to perspective errors, occlusions, or high velocity of the events. In this work we present a real time visual system for goal detection. Four cameras with high frame rates (200fps) are placed on the two sides of the goal lines. Four computers process the images acquired by the cameras detecting the ball position in real time; the processing result is sent to a central supervisor which evaluates the goal event probability and, when the goal is detected, forwards a warning signal to the referee that takes the final decision.

Visual recognition of fastening bolts for railroad maintenance

This paper presents a vision-based technique to automatically detect the absence of the fastening bolts that secure the rails to the sleepers. The images are pre-processed by using several combinations of WT and PCA methods.The final detecting system has been applied on a long sequence of real images showing a high reliability and robustness.

Goal detection in football by using support vector machines for classification

We present a technique for detecting goals during a football match by using images acquired by a single camera placed externally to the field. The method does not require the modification neither of the ball nor of the goalmouth. Due to the attitude of the camera with respect to the football ground, the system can be thought of as an electronic linesman which helps the referee in establishing the occurrence of a goal during a football match. The occurrence of the event is established by detecting the ball and comparing its position with respect to the location of the goalpost in the image. The ball detection technique relies on a supervised learning scheme called support vector machines for classification. The examples used for training are appropriately filtered version of views of the object to be detected, previously stored in the form of image patterns. We have extensively tested the technique on real images in which the ball is both fully visible and partially occluded. The performance of the proposed detection scheme are measured in terms of detection rate, false positive rate and precision in the ball localisation in image.

Model-based vision system for mobile robot position estimation

The development of an autonomous mobile robot is a central problem in artificial intelligence and robotics. A vision system can be used to recognize naturally occurring landmarks located in known positions. The problem considered here is that of finding the location and orientation of a mobile robot using a 3-D image taken by a CCD camera located on the robot. The naturally occurring landmarks that we use are the corners of the room extracted by an edge detection algorithm from a 2-D image of the indoor scene. Then, the location and orientation of the vehicle are calculated by perspective information of the landmarks in the scene of the room where the robot moves.