nils-herrmann/clean_scirepeval huggingface dataset:
Information theoretic sensor management for multi-target tracking with a single pan-tilt-zoom camera

Automatic multiple target tracking with pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the literature, most of them proposing simplistic scenarios. In this paper, we present a PTZ camera management framework which lies on information theoretic principles: at each time step, the next camera pose (pan, tilt, focal length) is chosen, according to a policy which ensures maximum information gain. The formulation takes into account occlusions, physical extension of targets, realistic pedestrian detectors and the mechanical constraints of the camera. Convincing comparative results on synthetic data, realistic simulations and the implementation on a real video surveillance camera validate the effectiveness of the proposed method.
An object oriented approach for object recognition and classification

Scene analysis with processing of multiple frames is considered for automatic recognition and tracking of moving objects. The importance of taking into account information about the objects in the design of flexible systems is emphasized. This goal is reached through an object-oriented approach to problem-solving. It is shown that abstract data type and inheritance relationship definitions over real-world entities can be efficiently used for object classification purposes. This has been verified in a traffic monitoring application. An efficient algorithmic solution that satisfies the real-time constraints of the problem is proposed.<<ETX>>
Recognizing human actions by fusing spatio-temporal appearance and motion descriptors

In this paper we propose a new method for human action categorization by using an effective combination of a new 3D gradient descriptor with an optic flow descriptor, to represent spatio-temporal interest points. These points are used to represent video sequences using a bag of spatio-temporal visual words, following the successful results achieved in object and scene classification. We extensively test our approach on the standard KTH and Weizmann actions datasets, showing its validity and good performance. Experimental results outperform state-of-the-art methods, without requiring fine parameter tuning.
Film semantic analysis

In the paper the problem of video segmentation and semantic analysis for video indexing and cinematographic language classification is addressed. Large archives of video sequences will remain a largely unexploitable resource of information, unless a straightforward way to describe their content is designed. The first step to be considered in this direction is the highlighting of the syntactic units of the visual speech. Therefore, video segmentation must focus on the editing procedures, through which the shots are fused into the video stream. This work proposes a statistically based new data-driven algorithm to detect cuts between two shots in a video sequence. On the contrary, the analysis of how the other editing techniques are performed, supplies a mathematical model, upon which the algorithms for the detection of fades, dissolves and mattes are based. A second part of this work is devoted to a higher level analysis, which involves the semantics of a film. On the basis of the common film editing rules, a method is presented to detect when a set of shots has unitary meaning. Experimental validation of the techniques proposed is thereby presented.
Two special digital processing systems for recognition of moving objects

Some special digital operators and recognition techniques are presented for digital image processing. Two complete digital systems are described. The first fast and relatively simple system uses non-linear smoothing and adaptive thresholding as a pre-processing and inertial invariant components to recognize moving objects. The second more complex system uses non-linear smoothing, edge detection and noise spike reduction as a pre-processing and FFT of the boundary distances from the centroid to recognize and track moving objects. Experiments of recognition and tracking of mechanical objects are reported.
Fast filtering in bridging Ethernet and FDDI networks: a CAM approach

The use of high-speed fiber-optic networks as backbones for lower speed LANS (large-area networks) is forcing the development of high-throughout bridges implementing fast address filtering algorithms. A filtering technique based on dedicated hardware employing CAM (content-addressable-memory) devices for an Ethernet-to-FDDI (fiber-distributed data-interface) bridge is presented. A simulation study shows that, in any reasonable scenario, more than half the search operations succeed in CAM, thus requiring a minimum processor effort. In a heavily loaded LAN this results in an average gain of about 140 mu s per received frame.<<ETX>>
A Desktop 3D Scanner Exploiting Rotation and Visual Rectification of Laser Profiles

We describe a low cost system for metric 3D scanning from uncalibrated images based on rotational kinematic constraints. The system is composed by a turntable, an offthe- shelf camera and a laser stripe illuminator. System operation is based on the construction of the virtual image of a surface of revolution (SOR), from which two imaged SOR cross-sections are obtained in an automatic way, and internal camera calibration is performed by exploiting the same object being scanned. Shape acquisition is finally obtained by laser profile rectification and collation. Experiments with real data are shown, providing an insight into both camera calibration and shape reconstruction performance. System accuracy appears to be adequate for desktop applications.
Using 3D spatial relationships for image retrieval by contents

Iconic environments for querying pictorial databases allow one to express spatial relationships between the objects in the images through appropriate icon positioning. In this paper, a visual environment is presented for image retrieval from a database of real world pictures. The environment differs from the other experiences reported in the literature, since the visual query is performed taking into account relationships between the objects in the scene that is represented in the image, rather than between the image subparts. A 3D iconic interface with 3D icons allows the user to define a virtual scene, which is used to perform the query. Images are stored in the database along with the descriptions of the 3D scenes they represent.<<ETX>>
Visual specification of virtual worlds

Verisimilitude to real world phenomena is the most appealing feature of virtual reality environments. A high degree of realism can be attained through a fine representation of the visual appearance of the actors playing in the virtual world, but a verisimilar replication of their behavior is necessary too. Preliminary results on a project aiming at the construction of a tool for the visual specification of the behavior pattern of virtual world actors are presented.<<ETX>>
Structured digital video indexing

This paper addresses the problems of film segmentation into its syntactic elements (the shots) and of their aggregation into semantic sets under constrained conditions: the shot/reverse-shot scenes (SRS). It then addresses the problem of describing the dynamic content of each shot through the analysis of camera motion. New techniques for edit effect detection and classification are herein proposed. A new algorithm is thereby introduced for automatic inferring the belonging of a set of shots to the same SRS scene. Finally, an algorithm is presented, which segments a sequence into regions having different motion types and extracts the camera motion. Extended performance analysis of the algorithms has been made on about 20 hours movies.
Retrieval of paintings using effects induced by color features

Image databases are now a subject of increasing attention in multimedia, for archiving and retrieval of images in the fields of art, history, medicine and industry, among others. From the psychological point of view, color perception is related to several factors including color features (brightness, chromaticity and saturation), surrounding colors, color spatial organization, observer memory/knowledge/experience etc. Paintings are an example where the message is contained more in the high-level color qualities and spatial arrangements than in the physical properties of colors. Starting from this observation, Johannes Itten (1961) introduced a formalism to analyze the use of color in art and the effects that this induces on the user's psyche. We present a system which translates the Itten theory into a formal language that allows to express the semantics associated with the combination of chromatic properties of color images. Fuzzy sets are used to represent low-level region properties. A formal language and a set of model checking rules are implemented that allow to define semantic clauses and verify the degree of truth by which they hold over an image.
Optical flow computation using extended constraints

Several approaches for optical flow estimation use partial differential equations to model changes in image brightness throughout time. A commonly used equation is the so-called optical flow constraint (OFC), which assumes that the image brightness is stationary with respect to time. More recently, a different constraint referred to as the extended optical flow constraint (EOFC) has been introduced, which also contains the divergence of the flow field of image brightness. There is no agreement in the literature about which of these constraints provides the best estimation of the velocity field. Two new solutions for optical flow computation are proposed, which are based on an approximation of the constraint equations. The two techniques have been used with both EOFC and OFC constraint equations. Results achieved by using these solutions have been compared with several well-known computational methods for optical flow estimation in different motion conditions. Estimation errors have also been measured and compared for different types of motion.
Geometric tampering estimation by means of a SIFT-based forensic analysis

In many application scenarios digital images play a basic role and often it is important to assess if their content is realistic or has been manipulated to mislead watcher's opinion. Image forensics tools provide answers to similar questions. This paper, in particular, focuses on the problem of detecting if a feigned image has been created by cloning an area of the image onto another zone to make a duplication or to cancel something awkward. The proposed method is based on SIFT features and allows both to understand which are the image points involved in the counterfeit attack and, furthermore, to recover the parameters of the geometric transformation. Experimental results are provided to witness the powerfulness of the proposed technique.
Using 3D and Ancillary Media to Train Construction Workers

Advances in graphic engines and software tools have facilitated the development of visual interfaces based on 3D virtual environments (VEs). These interfaces use interactive 3D graphics to represent visual and spatial information and allow natural interaction with direct object manipulation. Particularly in the training field, interactive 3D graphics offers effective, near-real-world representations, supporting learning-by-doing and case-based reasoning approaches. Although researchers have proposed and practiced many development guidelines on 2D graphical user interfaces, few contributions have addressed the systematic development of user interfaces based on 3D graphics and their possible extension to other media. We've addressed this problem in the construction of several VEs and we've organized our experience in a set of guidelines. We demonstrate their use by describing a virtual training environment called VECWIT (Virtual Environments for Construction Workers' Instruction and Training) that we developed to test the suitability of a VE as a complementary tool supporting education and training for construction workers' safety.
Device-tagged feature-based localization and mapping of wide areas with a PTZ camera

This paper proposes a new method for estimating and maintaining over time the pose of a single Pan-Tilt-Zoom camera (PTZ). This is achieved firstly by building offline a keypoints database of the scene; then, in the online step, a coarse localization is obtained from camera odometry and finally refined by visual landmarks matching. A maintenance step is also performed at runtime to keep updated the geometry and appearance of the map.
Person Detection Using Temporal and Geometric Context with a Pan Tilt Zoom Camera

In this paper we present a system that integrates automatic camera geometry estimation and object detection from a Pan Tilt Zoom camera. We estimate camera pose with respect to a world scene plane in real-time and perform human detection exploiting the relative space-time context. Using camera self-localization, 2D object detections are clustered in a 3D world coordinate frame. Target scale inference is further exploited to reduce the number of false alarms and to increase also the detection rate in the final non-maximum suppression stage. Our integrated system applied on real-world data shows superior performance with respect to the standard detector used.
RFID-based Solutions for User Profiling in Interactive Exhibits

In this paper we present a work-in-progress interactive exhibit for the museum of Onna, a little town near to L'Aquila (Italy), almost completely destroyed by the earthquake of April 2009. The installation will be developed as an environment in which visitors of the museum can interact with a natural interaction system and then discover the history of the disaster via rich multimedia contents. Visitors are detected through the adoption of an RFID-based technology, which allows to store their interaction history and build an interest profile used to enrich the experience. Different scenarios have been implemented and tested in order to evaluate the effectiveness of the proposed solution.
Understanding Sport Activities from Correspondences of Clustered Trajectories

Human activity recognition is a fundamental problem in computer vision with many applications such as video retrieval, automatic visual surveillance and human computer interaction. Sports represent one of the most viewed content on digital tv and the web. Automatically collected statistics of team sports game play represent actionable information for many end users such as coaches and broadcast speakers. Many computer vision methods applied to sport activity classification are often based on multi-camera setups, player tracking and exploit information on the ground-plane. In this work we overcome this limitations and propose an approach that exploits the spatio-temporal structure of a video grouping local spatio-temporal features unsupervisedly. Our robust representation allows to measure video similarity making correspondences among arbitrary patterns. We tested our method on two dataset of Volleyball and Soccer actions outperforming previous results by a large margin. Finally we show how our representation allows to highlight discriminative regions for each action.
Performance Analysis of Two Different Algorithms for Ethernet-FDDI Interconnection

Fiber Distributed Data Interface (FDDI) local area networks (LAN's) are used either as high-speed links between computers and peripherals, or as backbones for lower-speed LAN's, such as Ethernet and Token Ring. The availability of such a high-speed channel will lead to the implementation of high-performance distributed environments spread over a wider area than that allowed by commonly used LAN's. The performance of such distributed environments will strongly depend on that of the interconnecting devices. In this paper, two different algorithms for packet filtering are discussed, referring to bridges interconnecting Ethernet LAN's to FDDI backbones. Algorithm performance is compared with respect to 1) the traffic increase produced on a local Ethernet, and 2) the maximum allowed traffic on remote Ethernets.
Design and performance evaluation of a high throughput Ethernet/FDDI interconnect

A filtering algorithm, called MFA, that sets the minimum power requirements for a bridge connecting an Ethernet LAN to a fiber-distributed data interface (FDDI) backbone is introduced. With current technology, however, even MFA cannot be implemented in software at an acceptable speed. A hardware solution based on the use of content-addressable memories (CAMs) is described, and a comparison is made between hardware and software solutions through analytical and simulation models.<<ETX>>
Fisher Encoded Convolutional Bag-of-Windows for Efficient Image Retrieval and Social Image Tagging

In this paper we present an efficient and accurate method to aggregate a set of Deep Convolutional Neural Network (CNN) responses, extracted from a set of image windows. CNN features are usually computed on the whole frame or with a dense multi scale approach. There is evidence that using multiple windows yields a better image representation nonetheless it is still not clear how windows should be sampled and how CNN responses should be aggregated. Instead of sampling the image densely in scale and space we show that selecting a few hundred windows is enough to obtain an effective image signature. We show how to use Fisher Vectors and PCA to obtain a short and highly descriptive signature that can be used effectively for image retrieval. We test our method on two relevant computer vision tasks: image retrieval and image tagging. We report state-of-the art results for both tasks on three standard datasets.
Social media annotation

The large success of online social platforms for creation, sharing and tagging of user-generated media has lead to a strong interest by the multimedia and computer vision communities in research on methods and techniques for annotating and searching social media. Visual content similarity, geo-tags and tag co-occurrence, together with social connections and comments, can be exploited to perform tag suggestion as well as to perform content classification and clustering and enable more effective semantic indexing and retrieval of visual data. However there is need to countervail the relatively low quality of these metadata user produced tags and annotations are known to be ambiguous, imprecise and/or incomplete, overly personalized and limited - and at the same time take into account the `web-scale' quantity of media and the fact that social network users continuously add new images and create new terms. We will review the state of the art approaches to automatic annotation and tag refinement for social images and discuss extensions to tag suggestion and localization in web video sequences.
Highlight extraction in soccer videos

Live logging and posterity logging are the two basic applications for video databases. The former aims at providing effective annotation of video in quasi-real time and supports extraction of meaningful clips from the live stream; the latter provides annotation for later reuse of video material and is the prerequisite for retrieval by content from video digital libraries. Both require that information is adequately structured with interchange format and that annotation is performed, to a great extent, automatically. The video information structure must encompass both low-intermediate level video organization and event relationships that define specific highlights and situations. Analysis of the visual data of the video stream permits hints to be extracted, events identified and highlights detected. All of this must be supported by a-priori knowledge of the subject and effective reasoning engines capable of capturing the inherent semantics of the visual events.
Querying by photographs: using virtual reality for content-based image retrieval

The success of the 'query by example' paradigm for content based retrieval has lowered the interest of the research community in the development of new interaction paradigms. Nevertheless, in many contexts this can be inadequate, due to the difficulty for many users to paint significant examples and the impossibility-in many cases-of reproducing complex scenes. 3D interfaces and virtual reality based interaction paradigms offer new possibilities to overcome these limitations. In the prototype system presented, a virtual world can be interactively customized by adding objects to a predefined scene and editing object properties in terms of colours and textures. The system allows the user to navigate the 3D scene. While navigating the virtual world, the user is given the opportunity of taking some photographs used to query by content a database of images.
Soccer Video Annotation Using Ontologies Extended with Visual Prototypes

This paper presents multimedia ontologies, where visual prototypes are added as specialization of concepts defined in a traditional linguistic ontology. The more general concepts of the sport domain (e.g. play/break, crowd, etc.) are put in correspondence with the more general visual features of the video like color and texture, while the more specific concepts of the soccer domain (e.g. highlights such as attack actions) are put in correspondence with domain specific visual feature like the soccer playfield and the players. A solution for the implementation of multimedia ontologies for the soccer video domain and a method to perform automatic soccer video annotation using these extended ontologies is shown. Experimental results for automatic annotation of soccer videos containing generic concepts are presented.
A perspective view on visual information retrieval systems

Visual information retrieval is a new subject of research in information technology. Its purpose is to retrieve, from a database, images or image sequences that are relevant to a query. It is an extension of traditional information retrieval so to include visual media. Interactivity with visual content is essential to visual information retrieval. Visual elements such as color, texture and object shape, spatial relationship-directly related to perceptual aspects of image content, together with high-level concepts-the meaning of objects and scenes in the images, are used as clues for retrieving images with similar content from a database. The need for visual information retrieval has become apparent recently, as more and more visual information has become available in digital archives. The emergence of multimedia as the "new" technology and the possibility of sharing and distributing image/video data through large-bandwidth computer networks have made evident the importance and need of tools for retrieving visual information. Most visual information retrieval systems and experiences are prototypes that have been developed in university laboratories or research institutes. Very few commercial products are available, offering limited capabilities. Effectiveness of most solutions has still to be verified in real application contexts. Despite the fact that an increasing number of researchers are attracted by this research subject, many unexplored areas still exist and research is in its very early infancy. The paper discusses research directions in visual information retrieval.
Common visual cues for sports highlights detection

Automatic annotation of semantic events allows effective retrieval of video content. We present automatic annotation of sports highlights for some of the principal sports types. They are obtained by detecting and tracking a limited number of visual cues common to each sport. Highlights are represented as atomic entities at the semantic level. They have a limited temporal extension and can be modeled as the spatio-temporal concatenation of specific events. Visual cues encode position and speed information coming from the camera and from the objects/athletes that are present in the scene, and are estimated automatically from the video stream. Algorithms for model checking and for visual cue estimation are discussed. as well as applications of the representation to different sports domains