Pier Luigi Mazzeo

Color Brightness Transfer Function evaluation for non overlapping multi camera tracking

People Tracking in multiple cameras is of great interest for wide area video surveillance systems. Multi-camera tracking with non-overlapping fields of view (FOV) involves the tracking of people in the blind region and their correspondence matching across cameras. We consider these problems in this paper. We propose a multi camera architecture for wide area surveillance and a real time people tracking algorithm across non overlapping cameras. We compared different methods to evaluate the color Brightness Transfer Function (BTF) between non overlapping cameras. These approaches are based on a testing phase during which the color histogram mapping, between pairs of images of the same object observed in the different field of views, is carried out. The experimental results compare two different transfer functions and demonstrate their limits in people association when a new person enters in one camera FOV.

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.

Real-time multiview analysis of soccer matches for understanding interactions between ball and players

In the last decade, several research efforts have been undertaken in soccer video analysis. This increasing interest is motivated by the possible applications over a wide spectrum of topics: indexing, summarization, video enhancement, team and players statistics, tactic analysis, referees support, etc. Soccer video analysis requires different challenging tasks: ball and players have to be localized in each frame, tracked over time and, above all, their interactions have to be detected and analyzed. The latter task is fundamental, especially for statistics and referee decision support purposes, but, unfortunately, it has not received adequate attention from the scientific community. In this paper a multi-view system able to understand in real time the interactions between the ball and the players is presented. 3D ball trajectories are extracted by triangulation from multiple cameras and used to detect the interactions between the players and the ball. Inference processes are then introduced to determine the player kicking the ball and to fix the instant of the interaction. The system has been tested during several matches of the Italian first division soccer championship and experimental proofs of its effectiveness are reported.

HSV and RGB color histograms comparing for objects tracking among non overlapping FOVs, using CBTF

Object tracking over wide-areas, such as an airport, the downtown of a large city or any large public area, is done by multiple cameras. Especially in realistic application, those cameras have non overlapping Field of Views (FOVs). Multiple camera tracking is very important to establish correspondence among detected objects across different cameras. In this paper we investigate color histogram techniques to evaluate inter-camera tracking algorithm based on object appearances. We compute HSV and RGB color histograms in order to evaluate their performance in establishing correspondence between object appearances in different FOVs before and after Cumulative Brightness Transfer Function (CBTF).

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.

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.

Active Surveillance of Dynamic Environments using a Multi-Agent System

Abstract Mobility and multi-functionality have been recognized as being basic requirements for the development of fully automated surveillance systems in realistic scenarios. Nevertheless, problems such as active control of heterogeneous mobile agents, integration of information from fixed and moving sensors for high-level scene interpretation, and mission execution are open. This paper describes recent and current research of the authors concerning the design and implementation of a multi-agent surveillance system, using static cameras and mobile robots. The proposed solution takes advantage of a distributed control architecture that allows the agents to autonomously handle general-purpose tasks, as well as more complex surveillance issues. The various agents can either take decisions and act with some degree of autonomy, or cooperate with each other. This paper presents an overview of the system architecture and of the algorithms involved in developing such an autonomous, multi-agent surveillance system.

Object Tracking by Non-overlapping Distributed Camera Network

People Tracking is a problem of great interest for wide areas video surveillance systems. In these large areas, it is not possible for a single camera to observe the complete area of interest. Surveillance systems architecture requires algorithms with the ability to track objects while observing them through multiple cameras. We focus our work on multi camera tracking with non overlapping fields of view (FOV). In particular we propose a multi camera architecture for wide area surveillance and a real time people tracking algorithm across non overlapping cameras. In this scenario it is necessary to track object both in intra-camera and inter-camera FOV. We consider these problems in this paper. In particular we have investigated different techniques to evaluate intra-camera and inter-camera tracking based on color histogram. For the intra-camera tracking we have proposed different methodologies to extract the color histogram information from each object patches. For inter-camera tracking we have compared different methods to evaluate the colour Brightness Transfer Function (BTF) between non overlapping cameras. These approaches are based on color histogram mapping between pairs of images of the same object in different FOVs. Therefore we have combined different methodology to calculate the color histogram in order to estimate different colour BTF performances. Preliminary results demonstrates that the proposed method combined with BTF outperform the performance in terms of matching rate between different cameras.

Non-invasive Soccer Goal Line Technology: A Real Case Study

In this paper, a real case study on a Goal Line Monitoring system is presented. The core of the paper is a refined ball detection algorithm that analyzes candidate ball regions to detect the ball. A decision making approach, by means of camera calibration, decides about the goal event occurrence. Differently from other similar approaches, the proposed one provides, as unquestionable proof, the image sequence that records the goal event under consideration. Moreover, it is non-invasive: it does not require any change in the typical football devices (ball, goal posts, and so on). Extensive experiments were performed on both real matches acquired during the Italian Serie A championship, and specific evaluation tests by means of an artificial impact wall and a shooting machine for shot simulation. The encouraging experimental results confirmed that the system could help humans in ambiguous goal line event detection.

Visual recognition of hexagonal headed bolts by comparing ICA to wavelets

In this paper we present vision-based techniques to automatically detect the absence of the fastening bolts that secure the rails to the sleepers. The inspection system uses images from a digital line scan camera installed under a train. This application is part of the most general problem of object recognition. In object recognition as in supervised learning, we often extract new features from original ones for the purpose of reducing the feature space dimensions and achieving better performances. The goal of this paper is to compare two techniques within the context of the hexagonal-headed bolts recognition in railway maintenance. The first technique is Wavelets Transform (WT), the second technique is Independent Component Analysis (ICA), a new method that produces spatially localized and statistically independent basis vector. The coefficients of the new representation in the ICA and WT subspace are supplied as input to a Support Vector Machine (SVM). A SVM classifier analyses the images in order to evaluate the pre-processing technique which could give the highest rate in detecting the presence of the bolts. Results in terms of detection rate and false positive rate are given in the paper.