The SmarTrack Project at FBK: Past and Ongoing Efforts on People Tracking for Surveillance and Monitoring

Progress in computer vision research is reshaping the video surveillance sector: high-tech companies are starting to offer CCTV based systems now empowered with Video Analytics, i.e. with software solutions able to generate meaningful alerts by analyzing video feeds. Most surveillance applications target people as their subject of study, where they move, how they behave, and what they carry (or leave unattended); people tracking is therefore becoming a ever more important functionality for new generation technology. In this document we summarize our efforts in realizing SmarTrack, a multi camera people tracker developed by FBK over the last few years. We detail main research results, development efforts and applications, and present current and future research directions

Are Induced Pluripotent Stem Cells a Step towards Modeling Pediatric Leukemias?

Despite enormous improvements in pre-clinical and clinical research, acute leukemia still represents an open challenge for pediatric hematologists; both for a significant relapse rate and for long term therapy-related sequelae. In this context, the use of an innovative technology, such as induced pluripotent stem cells (iPSCs), allows to finely reproduce the primary features of the malignancy and can be exploited as a model to study the onset and development of leukemia in vitro. The aim of this review is to explore the recent literature describing iPSCs as a key tool to study different types of hematological malignancies, comprising acute myeloid leukemia, non-down syndrome acute megakaryoblastic leukemia, B cell acute lymphoblastic leukemia, and juvenile myelomonocytic leukemia. This model demonstrates a positive impact on pediatric hematological diseases, especially in those affecting infants whose onsets is found in fetal hematopoiesis. This evidence highlights the importance of achieving an in vitro representation of the human embryonic hematopoietic development and timing-specific modifications, either genetic or epigenetic. Moreover, further insights into clonal evolution studies shed light in the way of a new precision medicine era, where patient-oriented decisions and therapies could further improve the outcome of pediatric cases. Nonetheless, we will also discuss here the difficulties and limitations of this model

Role of CBL Mutations in Cancer and Non-Malignant Phenotype

CBL plays a key role in different cell pathways, mainly related to cancer onset and progres-sion, hematopoietic development and T cell receptor regulation. Somatic CBL mutations have been reported in a variety of malignancies, ranging from acute myeloid leukemia to lung cancer. Growing evidence have defined the clinical spectrum of germline CBL mutations configuring the so-called CBL syndrome; a cancer-predisposing condition that also includes multisystemic involvement char-acterized by variable phenotypic expression and expressivity. This review provides a comprehensive overview of the molecular mechanisms in which CBL exerts its function and describes the clinical manifestation of CBL mutations in humans

Computer vision based traffic monitoring system for multi-track freeways

Nowadays, development is synonymous with construction of infrastructure. Such road infrastructure needs constant attention in terms of traffic monitoring as even a single disaster on a major artery will disrupt the way of life. Humans cannot be expected to monitor these massive infrastructures over 24/7 and computer vision is increasingly being used to develop automated strategies to notify the human observers of any impending slowdowns and traffic bottlenecks. However, due to extreme costs associated with the current state of the art computer vision based networked monitoring systems, innovative computer vision based systems can be developed which are standalone and efficient in analyzing the traffic flow and tracking vehicles for speed detection. In this article, a traffic monitoring system is suggested that counts vehicles and tracks their speeds in realtime for multi-track freeways in Australia. Proposed algorithm uses Gaussian mixture model for detection of foreground and is capable of tracking the vehicle trajectory and extracts the useful traffic information for vehicle counting. This stationary surveillance system uses a fixed position overhead camera to monitor traffic

Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

This paper has been presented at : 5th International Visual Informatics Conference (IVIC 2017)This paper presents a comparative study of two deep learning models used here for vehicle detection. Alex Net and Faster R-CNN are compared with the analysis of an urban video sequence. Several tests were carried to evaluate the quality of detections, failure rates and times employed to complete the detection task. The results allow to obtain important conclusions regarding the architectures and strategies used for implementing such network for the task of video detection, encouraging future research in this topic.S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander. The authors wish to thank Dr. Fei Yin for the code for metrics employed for evaluations. Finally, we gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research. The data and code used for this work is available upon request from the authors

Recent research activities and result at FBK-TeV group

The Technologies of Vision (TeV) group is conducting leading research in the field of computer vision and image analysis, with particular focus on i) understanding of dynamic scenes populated by various kind of moving entities (people/vehicles) and ii) semantic annotation of images and videos aimed at the automated indexing of visual and multimedia material. Based on the results of the research activities, the TeV unit aims to develop new technologies and prototypes that promote innovation in society by stimulating technology-transfer to existing companies and/or the creation of new companies. In this report the most relevant recent results are briefly described, along with the current active projects and the recent technologies developed by the group

Robust Estimation of Correlation with Applications to Computer Vision

In this paper we compare to the standard correlation coefficient three estimators of similarity for visual patterns which are based on the L 2 and L 1 norms. The emphasis of the comparison is on the stability of the resulting estimates. Bias, efficiency, normality and robustness are investigated through Monte Carlo simulations in a statistical task, the estimation of the correlation parameter of a binormal distribution. The four estimators are then compared on two pattern recognition tasks: people identification through face recognition and book identification from the cover image. The similarity measures based on the L 1 norm prove to be less sensitive to noise and provide better performance than those based on L 2 norm . Keywords: template matching, robust statistics, correlation, face recognition, book recognition. 1. Introduction The estimation of similarity of patterns is a common low-level vision task which must be routinely performed by many computer vision systems. The Pear..

Recognition and Reconstruction of Partially Occluded Objects

A new automatic system for the recognition and reconstruction of rescaled and/or rotated partially occluded objects is presented. The objects to be recognized are described by 2D views and each view is occluded by several half-planes. The whole object views and their visible parts `linear cuts` are then stored in a database. To establish if a region R of an input image represents an object possibly occluded, the system generates a set of linear cuts of R and compare them with the elements in the database. Each linear cut of R is associated to the most similar database linear cut. R is recognized as an instance of the object O if the majority of the linear cuts of R are associated to a linear cut of views of O. In the case of recognition, the system reconstructs the occluded part of $R$ and determines the scale factor and the orientation in the image plane of the recognized object view. The system has been tested on two different datasets of objects, showing good performance both in terms of recogniti on and reconstruction accuracy

Method for efficient target detection from images robust to occlusion

The method for efficient target detection from images robust to occlusion disclosed by the present invention detects the presence and spatial location of a number of objects in images. It consists in (i) an off-line method to compile an intermediate representation of detection probability maps that are then used by (ii) an on-line method to construct a detection probability map suitable for detecting and localizing objects in a set of input images efficiently. The method explicitly handles occlusions among the objects to be detected and localized, and objects whose shape and configuration is provided externally, for example from an object tracker. The method according to the present invention can be applied to a variety of objects and applications by customizing the method's input functions, namely the object representation, the geometric object model, its image projection method, and the feature matching function

Gaussian Process for RSS-based Localisation

Localisation making use of Wi-Fi Signal Strength is a well established research strand, where various approaches have been proposed by the community. In our work we focus on the problem of localizing a device by considering the Received Signal Strength (RSS) of signals the device receives from the wireless access points around it. We propose a two-parts localization method that makes use of the Gaussian Process technique to interpolate signal vectors received during a training phase in order to estimate the unknown position of the device during test. The proposed method has been tested on four publicly available databases proposed by other authors. The comparison with their results shows the superior performance of our novel approach