Publications

In this paper we describe a system for automatically analyzing old documents and creating hyper linking between different epochs, thus opening ancient documents to young people and to make them available on the web with old and current content. We propose a supervised learning approach to segment text and illustration of digitized old documents using a texture feature based on local correlation aimed at detecting the repeating patterns of text regions and differentiate them from pictorial elements. Moreover we present a solution to help the user in finding contemporary content connected to what is automatically extracted from the ancient documents.

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2, 700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art.

The problem of labeling the connected components (CCL) of a binary image is well-defined and several proposals have been presented in the past. Since an exact solution to the problem exists and should be mandatory provided as output, algorithms mainly differ on their execution speed. In this paper, we propose and describe YACCLAB, Yet Another Connected Components Labeling Benchmark. Together with a rich and varied dataset, YACCLAB contains an open source platform to test new proposals and to compare them with publicly available competitors. Textual and graphical outputs are automatically generated for three kinds of test, which analyze the methods from different perspectives. The fairness of the comparisons is guaranteed by running on the same system and over the same datasets. Examples of usage and the corresponding comparisons among state-of-the-art techniques are reported to confirm the potentiality of the benchmark.

The Internet of Things, whose main goal is to automatically predict users' desires, can find very interesting opportunities in the art and culture field, as the tourism is one of the main driving engines of the modern society. Currently, the innovation process in this field is growing at a slower pace, so the cultural heritage is a prerogative of a restricted category of users. To address this issue, a significant technological improvement is necessary in the culture-dedicated locations, which do not usually allow the installation of hardware infrastructures. In this paper, we design and validate a no-invasive indoor location-aware architecture able to enhance the user experience in a museum. The system relies on the user's smartphone and a wearable device (with image recognition and localization capabilities) to automatically deliver personalized cultural contents related to the observed artworks. The proposal was validated in the MUST museum in Lecce (Italy).

Long term tracking of people in unconstrained scenarios is still an open problem due to the absence of constant elements in the problem setting. The camera, when active, may move and both the background and the target appearance may change abruptly leading to the inadequacy of most standard tracking techniques. We propose to exploit a learning approach that considers the tracking task as a semi supervised learning (SSL) problem. Given few target samples the aim is to search the target occurrences in the video stream re-interpreting the problem as label propagation on a similarity graph. We propose a solution based on graph transduction that works iteratively frame by frame. Additionally, in order to avoid drifting, we introduce an update strategy based on an evolutionary clustering technique that chooses the visual templates that better describe target appearance evolving the model during the processing of the video. Since we model people appearance by means of covariance matrices on color and gradient information our framework is directly related to structure learning on Riemannian manifolds. Tests on publicly available datasets and comparisons with stateof- the-art techniques allow to conclude that our solution exhibit interesting performances in terms of tracking precision and recall in most of the considered scenarios.

Augmented user experiences in the cultural heritage domain are in increasing demand by the new digital native tourists of 21st century. In this paper, we propose a novel solution that aims at assisting the visitor during an outdoor tour of a cultural site using the unique first person perspective of wearable cameras. In particular, the approach exploits computer vision techniques to retrieve the details by proposing a robust descriptor based on the covariance of local features. Using a lightweight wearable board the solution can localize the user with respect to the 3D point cloud of the historical landmark and provide him with information about the details he is currently looking at. Experimental results validate the method both in terms of accuracy and computational effort. Furthermore, user evaluation based on real-world experiments shows that the proposal is deemed effective in enriching a cultural experience.

Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems.

With the increasing popularity of wearable cameras, such as GoPro or Narrative Clip, research on continuous activity monitoring from egocentric cameras has received a lot of attention. Research in hardware and software is devoted to find new efficient, stable and long-time running solutions; however, devices are too power-hungry for truly always-on operation, and are aggressively duty-cycled to achieve acceptable lifetimes. In this paper we present a wearable system for context change detection based on an egocentric camera with ultra-low power consumption that can collect data 24/7. Although the resolution of the captured images is low, experimental results in real scenarios demonstrate how our approach, based on Siamese Neural Networks, can achieve visual context awareness. In particular, we compare our solution with hand-crafted features and with state of art technique and propose a novel and challenging dataset composed of roughly 30000 low-resolution images.

The advent of modern approaches to education, like Massive Open Online Courses (MOOC), made video the basic media for educating and transmitting knowledge. However, IT tools are still not adequate to allow video content re-use, tagging, annotation and personalization. In this paper we analyze the problem of identifying coherent sequences, called scenes, in order to provide the users with a more manageable editing unit. A simple spectral clustering technique is proposed and compared with state-of-the-art results. We also discuss correct ways to evaluate the performance of automatic scene detection algorithms.

Document layout segmentation and recognition is an important task in the creation of digitized documents collections, especially when dealing with historical documents. This paper presents an hybrid approach to layout segmentation as well as a strategy to classify document regions, which is applied to the process of digitization of an historical encyclopedia. Our layout analysis method merges a classic top-down approach and a bottom-up classification process based on local geometrical features, while regions are classified by means of features extracted from a Convolutional Neural Network merged in a Random Forest classifier. Experiments are conducted on the first volume of the ``Enciclopedia Treccani'', a large dataset containing 999 manually annotated pages from the historical Italian encyclopedia.