In this paper, an accurate range sensor for the three-dimensional reconstruction of environments is designed and developed. Following the principles of laser profilometry, the device exploits a set of optical transmitters able to project a laser line on the environment. A high-resolution and high-frame-rate camera assisted by a telecentric lens collects the laser light reflected by a parabolic mirror, whose shape is designed ad hoc to achieve a maximum measurement error of 10 mm when the target is placed 3 m away from the laser source. Measurements are derived by means of an analytical model, whose parameters are estimated during a preliminary calibration phase. Geometrical parameters, analytical modeling and image processing steps are validated through several experiments, which indicate the capability of the proposed device to recover the shape of a target with high accuracy. Experimental measurements show Gaussian statistics, having standard deviation of 1.74 mm within the measurable range. Results prove that the presented range sensor is a good candidate for environmental inspections and measurements.

One of the first tasks executed by a vision system made of fixed cameras is the background (BG) subtraction and a particularly challenging context for real time applications is the athletic one because of illumination changes, moving objects and cluttered scenes. The aim of this work is to extract a BG model based on statistical likelihood able to process color filter array (CFA) images taking into account the intrinsic variance of each gray level of the sensor, named Likelihood Bayer Background (LBB). The BG model should be not so computationally complex while highly responsive to extract a robust foreground. Moreover, the mathematical operations used in the formulation should be parallelizable, working on image patches, and computationally efficient, exploiting the dynamics of a pixel within its integer range. Both simulations and experiments on real video sequences demonstrate that this BG model approach shows great performances and robustness during the real time processing of scenes extracted from a soccer match.

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.

Sports video research is a popular topic that has been applied to many prominent sports for a large spectrum of applications. In this paper we introduce a technology platform which has been developed for the tennis context, able to extract action sequences and provide support to coaches for players performance analysis during training and official matches. The system consists of an hardware architecture, devised to acquire data in the tennis context and for the specific domain requirements, and a number of processing modules which are able to track both the ball and the players, to extract semantic information from their interactions and automatically annotate video sequences. The aim of this paper is to demonstrate that the proposed combination of hardware and software modules is able to extract 3D ball trajectories robust enough to evaluate ball changes of direction recognizing serves, strokes and bounces. Starting from these information, a finite state machine based decision process can be employed to evaluate the score of each action of the game. The entire platform has been tested in real experiments during both training sessions and matches, and results show that automatic annotation of key events along with 3D positions and scores can be used to support coaches in the extraction of valuable information about players intentions and behaviours.

Background (BG) modelling is a key task in every computer vision system (CVS) independently of the final purpose for which it is designed. Even if many BG approaches exist (for example Mixture of Gaussians or Eigenbackground), they can not efficiently process real time videos due to the model complexity and to the high throughput of the video flux. One of the most challenging real time applications is the athletic scene processing, because, in this context, there are many critical aspects for defining a BG model: no a-priori knowledge of the static scene, sudden illumination changes and many moving objects that slow down the upgrade phase. The aim of this work is to provide an adaptive BG model able to deal with high frame rate videos (>= 100 fps) in real time processing, and suitable for smart cameras embedding, finding a good compromise between the model complexity and its responsiveness. Real experiments demonstrate that this BG model approach shows great performances and robustness during the real time processing of athletic video frames, up to 100 fps. Copyright 2014 ACM.

Person re-identification has increasingly become an interesting task in the computer vision field, especially after the well known terroristic attacks on theWorld Trade Center in 2001. Even if video surveillance systems exist since the early 1950s, the third generation of such systems is a relatively modern topic and refers to systems formed by multiple fixed or mobile cameras - geographically referenced or not - whose information have to be handled and processed by an intelligent system. In the last decade, researchers are focusing their attention on the person re-identification task because computers (and so video surveillance systems) can handle a huge amount of data reducing the time complexity of the algorithms. Moreover, some well known image processing techniques - i.e. background subtraction - can be embedded directly on cameras, giving modularity and flexibility to the whole system. The aim of this work is to present an appearance-based method for person re-identification that models the chromatic relationship between both different frames and different areas of the same frame. This approach has been tested against two public benchmark datasets (ViPER and ETHZ) and the experiments demonstrate that the person re-identification processing by means of intra frame relationships is robust and shows great results in terms of recognition percentage.

In this paper, an approach based on the analysis of variance (ANOVA) for the extraction of crop marks from aerial images is improved by means of preliminary analyses and semantic processing of the extracted objects. The paper falls in the field of digitalization of images for archaeology, assisting expert users in the detection of un-excavated sites. The methodology is improved by a preliminary analysis of local curvatures, able to determine the most suitable direction for the ANOVA formulation. Then, a semantic processing, based on the knowledge of the shape of the target wide line, is performed to delete false positive detections. Sample analyses are always performed on actual images and prove the capability of the method to discriminate the most significant marks, aiding archaeologists in the analysis of huge amount of data.

In this paper, a method to find, exploit and classify ambiguities in the results of a person re-identification PRID) algorithm is presented. We start from the assumption that ambiguity is implicit in the classical formulation of the re-identification problem, as a specific individual may resemble one or more subjects by the color of dresses or the shape of the body. Therefore, we propose the introduction of the AMbiguity rAte in REidentification (AMARE) approach, which relates the results of a classical PRID pipeline on a specific dataset with their effectiveness in re-identification terms, exploiting the ambiguity rate (AR). As a consequence, the cumulative matching curves (CMC) used to show the results of a PRID algorithm will be filtered according to the AR. The proposed method gives a different interpretation of the output of PRID algorithms, because the CMC curves are processed, split and studied separately. Real experiments demonstrate that the separation of the results is really helpful in order to better understand the capabilities of a PRID algorithm.

High resolution in distance (range) measurements can be achieved by means of accurate instrumentations and precise analytical models. This paper reports an improvement in the estimation of distance measurements performed by an omnidirectional range sensor already presented in literature. This sensor exploits the principle of laser triangulation, together with the advantages brought by catadioptric systems, which allow the reduction of the sensor size without decreasing the resolution. Starting from a known analytical model in two dimensions (2D), the paper shows the development of a fully 3D formulation where all initial constrains are removed to gain in measurement accuracy. Specifically, the ray projection problem is solved by considering that both the emitter and the receiver have general poses in a global system of coordinates. Calibration is thus made to estimate their poses and compensate for any misalignment with respect to the 2D approximation. Results prove an increase in the measurement accuracy due to the more general formulation of the problem, with a remarkable decrease of the uncertainty.

The knowledge on dolphins and whales, their habitat suitability and conservation status in the Mediterranean Sea is still rather heterogeneous and defective although these aspects represent fundamental currency for evaluating the anthropogenic impacts on the ecosystem functioning. Concerning the Ionian Sea (Central-eastern Mediterranean Sea), starting from 2009 an intense research activity was carried out by the Jonian Dolphin Conservation (JDC) and the Department of Biology University of Bari (UNIBA Local Research Unit CoNISMa), reducing the shortcomings in the basic scientific information in the Gulf of Taranto (Northern Ionian Sea). Sightings data were collected during standardized vessel-based surveys confirming the presence of 8 different species of cetaceans (Dimatteo et al., 2011; Fanizza et al., 2014; Carlucci et al., 2015 and 2016). Innovative field of research were opened from 2015 collaborating with the Muséum National d'Histoire Naturelle UMR-Paris, for the genetic study on the social structure in the striped dolphin Stenella coeruleoalba and the bottlenose dolphin Tursiops truncatus, as well as with the Institute of Intelligent Systems for Automation ISSIA-CNR, for an automatic system of photo-identification for the bottlenose dolphin, the Risso's dolphin Grampus griseus and the sperm whale Physeter macrocephalus. Lastly, a new study on the acoustic inventory of the striped dolphin and the Risso's dolphin was started from 2016 collaborating with the Institute for Coastal Marine Environment IAMC-CNR of Torretta Granitola. Finally, an intense education activity was carried out from 2009 by JDC as a structured project of Citizen Science involving school-children, students as well tourists on whale and dolphin watching boarding on 3 different vessels equipped for scientific surveys and monitoring programs. Moreover, a national monitoring along the Italian costs was coordinated by UNIBA on board of the training ships Palinuro and Vespucci in the framework of the dual use activity carried out in collaboration with the Italian Navy.

Computer vision is steadily gaining importance in many research fields, as its applications expand from traditional fields situation analysis and scene understanding in video surveillance to other scenarios. The sportive context can represent a perfect test-bed for many machine vision algorithms because of the large availability of visual data brought by wide spread cameras on a relatively high number of courts. In this paper we introduce a tennis ball detection and tracking method that exploits domain knowledge to effectively recognize ball positions and trajectories. A peculiarity of this approach is that it starts from a sparse but cluttered point cloud that evolves over time, basically working on 3D samples only. Experiments on real data demonstrate the effectiveness of the algorithm in terms of tracking accuracy and path following capability.

This paper analyzes with a new perspective the recent state of-the-art on gesture recognition approaches that exploit both RGB and depth data (RGB-D images). The most relevant papers have been analyzed to point out which features and classifiers best work with depth data, if these fundamentals are specifically designed to process RGB-D images and, above all, how depth information can improve gesture recognition beyond the limit of standard approaches based on solely color images. Papers have been deeply reviewed finding the relation between gesture complexity and features/methodologies suitability. Different types of gestures are discussed, focusing attention on the kind of datasets (public or private) used to compare results, in order to understand weather they provide a good representation of actual challenging problems, such as: gesture segmentation, idle gesture recognition, and length gesture invariance. Finally the paper discusses on the current open problems and highlights the future directions of research in the field of processing of RGB-D data for gesture recognition.

We live in the era of the fourth industrial revolution, where everything - from small objects to entire factories - is smart and connected, and we are also strongly accustomed to comforts and services, but emergent questions are arising. What are the consequences of human activities on terrestrial and aquatic/marine systems? And how does the loss of biodiversity alter the integrity and functioning of ecosystems? It is reasonable to assert that there are correlations between the anthropic pressure and degradation of natural habitats and loss in biodiversity. In fact, the alteration of ecosystem structure affects ecosystem services and resilience, the level of perturbation that an ecosystem can withstand without shifting to an alternative status providing fewer benefits to humans [1]. To that regards, the research studies on cetacean species distribution and conservation status along with their habitats can give an idea of the current impact of human pressure on marine biodiversity and its ecosystem services, being both dolphins and whales key species in the marine food webs. However, although the inherent complexity of food-web dynamics often makes difficult to investigate and quantify the role of marine mammals in the ecosystem [2], the challenge to investigate their ecological significance is leading and highly informative when facing human induced environmental changes from local to global scales. For this reason, dedicated research activities have been performed in the last years to standardize the best practices for sampling and collecting scientific relevant information on the cetaceans in the Gulf of Taranto (Northern Ionian Sea in the Central-Eastern Mediterranean Sea) [3, 4, 5, 6]. Standardized scientific protocols and technological innovations have been brought by integrating interdisciplinary approaches: a genetic study on dolphin's social structure, an automated photo-identification, assisted by intelligent unsupervised algorithms and the study of acoustic signals. Finally, education and citizen science were applied as fundamental to raise awareness on the need of marine environmental protection among the active population, from children to adults.

Tennis player silhouette extraction is a preliminary step fundamental for any behavior analysis processing. Automatic systems for the evaluation of player tactics, in terms of position in the court, postures during the game and types of strokes, are highly desired for coaches and training purposes. These systems require accurate segmentation of players in order to apply posture analysis and high level semantic analysis. Background subtraction algorithms have been largely used in sportive context when fixed cameras are used. In this paper an innovative background subtraction algorithm is presented, which has been adapted to the tennis context and allows high precision in player segmentation both for the completeness of the extracted silhouettes. The algorithm is able to achieve interactive frame rates with up to 30 frames per second, and is suitable for smart cameras embedding. Real experiments demonstrate that the proposed approach is suitable in tennis contexts.