Marco Leo

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.

On the study of feature extraction methods for an electronic nose

In this study, we analyzed the transient of microsensors based on tin oxide sol–gel thin film. A novel method to this research field for data analysis and discrimination among different volatile organic compounds is presented. Moreover; several feature extraction methods have been considered, both steady-state (fractional change, relative, difference and log) and transient (Fourier and wavelet descriptors, integral and derivatives) information. Feature extraction methods have been validated qualitatively (by using principal component analysis) and quantitatively on the classification rate (by using a radial basis function neural network). # 2002 Elsevier Science B.V. All rights reserved.

A review of vision-based systems for soccer video analysis

This paper presents a survey of soccer video analysis systems for different applications: video summarization, provision of augmented information, high-level analysis. Computer vision techniques have been adapted to be applicable in the challenging soccer context. Different semantic levels of interpretation are required according to the complexity of the corresponding applications. For each application area we analyze the computer vision methodologies, their strengths and weaknesses and we investigate whether these approaches can be applied to extensive and real time soccer video analysis.

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.

A Semi-automatic System for Ground Truth Generation of Soccer Video Sequences

The problem of ground truth generation is fundamental for many approaches of computer vision and image processing. In order to test algorithms for object segmentation, object tracking, object interactions, it is necessary to have image sequences in which the ground truth is determined in an objective way. In the context of visual surveillance where many people moves in the scene occluding each other, it could be very complex and hard the work of generating for each image the position of all the moving objects and maintain this information for all the period in which they remain in the scene. In this paper we propose a semi-automatic system that generates an initial ground truth estimation, and then provides a user-friendly interface to manually validate or correct the track results. The proposed system has been tested on some soccer video sequences that have been published on-line for being available to the scientific community, but it can be used also in other surveillance contexts.

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.

An Abandoned/Removed Objects Detection Algorithm and Its Evaluation on PETS Datasets

In this paper, a new method for a robust and efficient analysis of video sequences is presented; it allows the extraction of foreground objects and the classification of static foreground regions as abandoned or removed objects (ghosts). As a first step, the moving regions in the scene are detected by subtracting to the current frame a background model continuously adapted. Then, a shadow removing algorithm is used to extract the real shape of detected objects. Finally, moving objects are classified as abandoned or removed by matching the boundaries of static foreground regions. The method was successfully tested on both real image sequences acquired in our laboratory and some sequences from the PETS 2006 Datasets.

A neural system for eye detection in a driver vigilance application

The problem of eye detection for a driver vigilance system is very important in order to monitor driver fatigue, inattention, and lack of sleep. A neural classifier has been applied to recognize the eyes in the image, selecting the couple of regions candidate to 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. Different experiments have been carried out on images of subjects with different eyes colors, some of them wearing glasses. Tests showed robustness with respect to situations such as eyes partially occluded. In particular when applied to images where people have the eyes closed the proposed algorithm correctly reveals the absence of eyes. Eyes tracking in an image sequence is applied to detect eye closure that can be dangerous if persists for a long period.