Massimiliano Nitti

A ball detection algorithm for real soccer image sequences

A large number of methods for circle detection has been studied in the last years for numerous image processing applications. The application domain considered in this paper is the soccer game. To identify the ball in soccer images is very important in order to evaluate the goal event. This domain is challenging as a great number of problems has to be managed, such as occlusions, shadows, objects similar to the ball, real time processing. The aim of this work is to present the results of a number of experiments obtained by using a modified version of the directional circle Hough transform. Different lighting conditions have been considered since they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light the ball appears as a spherical cap then the search of the ball has been modified in order to manage those situations. A large number of experiments has been carried out showing that the proposed method obtains an high detection score.

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.

Rail corrugation detection by Gabor filtering

Inspection of the rail state in order to detect defects is one of the basic tasks in railway maintenance. Rail defects exhibit different properties and are divided in various categories relating to the type and position of flaws on the rail. We propose a technique, based on texture analysis of the rail surface, to detect and classify a particular class of defects: corrugation.

A Modified Iterative Closest Point Algorithm for 3D Point Cloud Registration

In this article, an accurate method for the registration of point clouds returned by a 3D rangefinder is presented. The method modifies the well-known iterative closest point (ICP) algorithm by introducing the concept of deletion mask. This term is defined starting from virtual scans of the reconstructed surfaces and using inconsistencies between measurements. In this way, spatial regions of implicit ambiguities, due to edge effects or systematical errors of the rangefinder, are automatically found. Several experiments are performed to compare the proposed method with three ICP variants. Results prove the capability of deletion masks to aid the point cloud registration, lowering the errors of the other ICP variants, regardless the presence of artifacts caused by small changes of the sensor view-point and changes of the environment.

A Visual Tracking Algorithm for Real Time People Detection

In this paper we present a multi-people-tracking algorithm which is able to detect and track humans in complex situations with varying light conditions, high frame rate, and real time processing. We propose a stochastic approach for foreground people tracking based on the evaluation of the maximum a posteriori probability (MAP). The algorithm evaluates geometrical information on the blob overlapping and does not require the feature extraction to track the single object. Experimental tests have been carried out on soccer image sequence in which some players enter into the camera view and remain for some time.

An Unsupervised Approach for Segmentation and Clustering of Soccer Players

In this work we consider the problem of soccer team discrimination. The approach we propose starts from the monocular images acquired by a still camera. The first step is the soccer player detection, performed by means of background subtraction. An algorithm based on pixels energy content has been implemented in order to detect moving objects. The use of energy information, combined with a temporal sliding window procedure, allows to be substantially independent from motion hypothesis. Colour histograms in RGB space are extracted from each player, and provided to the unsupervised classification phase. This is composed by two distinct modules: firstly, a modified version of the BSAS clustering algorithm builds the clusters for each class of objects. Then, at runtime, each player is classified by evaluating its distance, in the features space, from the classes previously detected. Algorithms have been tested on different real soccer matches of the Italian Serie A.

An Embedded Vision System for Real-Time Autonomous Localization Using Laser Profilometry

In this paper, we propose an embedded vision system based on laser profilometry able to get the pose of a vehicle and its relative displacements with reference to the constitutive media of a structured environment. Fundamental equations for laser triangulation are developed and encoded for their actual implementation on an embedded system. It is made of a laser source that projects a line-shaped beam onto the environment and an on-chip camera able to frame the laser light. Images are then sent to the inexpensive Raspberry Pi onboard computer, which is responsible for processing tasks. For the first time, laser profilometry is coupled with the correlation of laser signatures on a low-cost and low-resource processing board for vehicle localization purposes. Several validation tests of the proposed sensor have proven the effectiveness of the system with respect to commercially available sensors such as inductive sensors and standard odometers, which fail when the vehicle crosses path interceptions or its wheels undergo unavoidable slippages. Moreover, further comparisons with other vision-based techniques have also proven the good performances of this embedded system for real-time localization of vehicles.

High-Resolution Laser Scanning for Three-Dimensional Inspection of Drilling Tools

A high-resolution vision system for the inspection of drilling tools is presented. A triangulation-based laser scanner is used to extract a three-dimensional model of the target aimed to the fast detection and characterization of surface defects. The use of two orthogonal calibrated handlings allows the achievement of precisions of the order of few microns in the whole testing volume and the prevention of self-occlusions induced on the undercut surfaces of the tool. Point cloud registration is also derived analytically to increase to strength of the measurement scheme, whereas proper filters are used to delete samples whose quality is below a reference threshold. Experimental tests are performed on calibrated spheres and different-sized tools, proving the capability of the presented setup to entirely reconstruct complex targets with maximum absolute errors between the estimated distances and the corresponding nominal values below 12 μm.

Soccer ball detection by comparing different feature extraction methodologies

This paper presents a comparison of different feature extraction methods for automatically recognizing soccer ball patterns through a probabilistic analysis. It contributes to investigate different well-known feature extraction approaches applied in a soccer environment, in order tomeasure robustness accuracy and detection performances. This work, evaluating differentmethodologies, permits to select the one which achieves best performances in terms of detection rate and CPU processing time. The effectiveness of the differentmethodologies is demonstrated by a huge number of experiments on real ball examples under challenging conditions.