nils-herrmann/clean_scirepeval huggingface dataset:
A parallel vision subsystem for robotic inspection and manipulation

The paper describes an SIMD massively parallel computer conceived for robot vision, and presents an application where the system is installed on a mobile robot to support inspection and manipulation tasks. The system consists of a scalable array of up to 4 K single-bit processors controlled by a general-purpose microcomputer through a dedicated interface. The system's architecture is overviewed, addressing the available prototype as well as the functional units currently under design. The authors provide examples of image analysis goals that can be efficiently reached using spatially-organized parallel architectures such as SIMD arrays and give measurements of present performances. Using code profiles the authors also evaluate the speedup associated with the configuration being designed.<<ETX>>
A real-time system for abnormal path detection

This paper proposes a real-time system capable to extract and model object trajectories from a multi-camera setup with the aim of identifying abnormal paths. The trajectories are modeled as a sequence of positional distributions (2D Gaussians) and clustered in the training phase by exploiting an innovative distance measure based on a global alignment technique and Bhattacharyya distance between Gaussians. An on-line classification procedure is proposed in order to on-the-fly classify new trajectories into either "normal" or "abnormal" (in the sense of rarely seen before, thus unusual and potentially interesting). Experiments on a real scenario will be presented.
Body Part Based Re-Identification from an Egocentric Perspective

With the spread of wearable cameras, many consumer applications ranging from social tagging to video summarization would greatly benefit from people re-identification methods capable of dealing with the egocentric perspective. In this regard, first-person camera views present such a unique setting that traditional re-identification methods results in poor performance when applied to this scenario. In this paper, we present a simple but effective solution that overcomes the limitations of traditional approaches by dividing people images into meaningful body parts. Furthermore, by taking into account human gaze information concerning where people look at when trying to recognize a person, we devise a meaningful way to weight the contributions of different bodyparts. Experimental results validate the proposal on a novel egocentric re-identification dataset, the first of its kind, showing that the performance increases when compared to current state of the art on egocentric sequences is significant.
Neighbor cache prefetching for multimedia image and video processing

Cache performance is strongly influenced by the type of locality embodied in programs. In particular, multimedia programs handling images and videos are characterized by a bidimensional spatial locality, which is not adequately exploited by standard caches. In this paper we propose novel cache prefetching techniques for image data, called neighbor prefetching, able to improve exploitation of bidimensional spatial locality. A performance comparison is provided against other assessed prefetching techniques on a multimedia workload (with MPEG-2 and MPEG-4 decoding, image processing, and visual object segmentation), including a detailed evaluation of both the miss rate and the memory access time. Results prove that neighbor prefetching achieves a significant reduction in the time due to delayed memory cycles (more than 97% on MPEG-4 with respect to 75% of the second performing technique). This reduction leads to a substantial speedup on the overall memory access time (up to 140% for MPEG-4). Performance has been measured with the PRIMA trace-driven simulator, specifically devised to support cache prefetching.
A reasoning engine for intruders' localization in wide open areas using a network of cameras and RFIDs

Wide open areas represent challenging scenarios for surveillance systems, since sensory data can be affected by noise, uncertainty, and distractors. Therefore, the tasks of localizing and identifying targets (e.g., people) in such environments suggest to go beyond the use of camera-only deployments. In this paper, we propose an innovative system relying on the joint use of cameras and RFIDs, allowing us to “map” RFID tags to people detected by cameras and, thus, highlighting potential intruders. To this end, sophisticated filtering techniques preserve the uncertainty of data and overcome the heterogeneity of sensors, while an evidential fusion architecture, based on Transferable Belief Model, combines the two sources of information and manages conflict between them. The conducted experimental evaluation shows very promising results.
Temporal analysis of cache prefetching strategies for multimedia applications

Prefetching is a widely adopted technique for improving performance of cache memories. Performances are typically affected by the design parameters, such as cache size and associativity, but also by the type of locality embodied in the programs. In particular multimedia tools and programs handling images and video are characterized by a bi-dimensional spatial locality that could be greatly exploited by the inclusion of prefetching in the cache architecture. In this paper we compare some prefetching techniques for multimedia programs (such as MPEG compression, image processing, visual object segmentation) by performing a detailed evaluation of the memory access time. The goal is to prove that a significant speedup can be achieved by using either standard prefetching techniques (such as OBL or adaptive prefetching) or some innovative and image-oriented prefetching methods like the neighbor prefetching described in the paper. Performance are measured with the PRIMA trace-driven simulator.
Learning Graph Cut Energy Functions for Image Segmentation

In this paper we address the task of learning how to segment a particular class of objects, by means of a training set of images and their segmentations. In particular we propose a method to overcome the extremely high training time of a previously proposed solution to this problem, Kernelized Structural Support Vector Machines. We employ a one-class SVM working with joint kernels to robustly learn significant support vectors (representative image-mask pairs) and accordingly weight them to build a suitable energy function for the graph cut framework. We report results obtained on two public datasets and a comparison of training times on different training set sizes.
Appearance tracking by transduction in surveillance scenarios

We propose a formulation of people tracking problem as a Transductive Learning (TL) problem. TL is an effective semi-supervised learning technique by which many classification problems have been recently reinterpreted as learning labels from incomplete datasets. In our proposal the joint exploitation of spectral graph theory and Riemannian manifold learning tools leads to the formulation of a robust approach for appearance based tracking in Video Surveillance scenarios. The key advantage of the presented method is a continuously updated model of the tracked target, used in the TL process, that allows to on-line learn the target visual appearance and consequently to improve the tracker accuracy. Experiments on public datasets show an encouraging advancement over alternative state-of the-art techniques.
Detection of luminosity profiles of elongated shapes

A novel technique for identifying elongated shapes in grey-scale images is presented. The method provides the detection and identification of elongated shapes not only modelling their principal direction, but also reconstructing the transversal luminosity profile. The approach is proposed starting from the gradient-weighted Hough transform, endowing the Hough space with more complete information about the luminance gradient of the image. This paper presents and discusses the algorithm devised to implement the method on discretized data. As an example of application, we present results on images from mechanical pieces, where real and false defects are discriminated through effective reconstruction of their luminosity profile.
Image analysis and rule-based reasoning for a traffic monitoring system

The paper presents an approach for detecting vehicles in urban traffic scenes by means of rule-based reasoning on visual data. The strength of the approach is its formal separation between the low-level image processing modules and the high-level module, which provides a general-purpose knowledge-based framework for tracking vehicles in the scene. The image-processing modules extract visual data from the scene by spatio-temporal analysis during daytime, and by morphological analysis of headlights at night. The high-level module is designed as a forward chaining production rule system, working on symbolic data, i.e., vehicles and their attributes (area, pattern, direction, and others) and exploiting a set of heuristic rules tuned to urban traffic conditions. The synergy between the artificial intelligence techniques of the high-level and the low-level image analysis techniques provides the system with flexibility and robustness.
Predictive and Probabilistic Tracking to Detect Stopped Vehicles

Many techniques and models have been proposed for vehicles surveillance in highways. In the past, tracking algorithms based on Kalman filter have been largely used for their efficiency in the prediction and low computational cost. However, predictive filters cannot solve long-lasting occlusions. In this paper, we propose a new mixed predictive and probabilistic tracking that exploits the advantages of predictive filters for moving vehicles and adopts probabilistic and appearance-based tracking for stopped vehicles. The proposed tracking is part of a complete video surveillance system, oriented to control tunnels and highways from cluttered views, that is implemented in an embedded DSP platform and provides background suppression, a novel shadow detection algorithm, tracking, and scene recognition module. The experimental results are obtained over several hours of videos acquired in pre-existing platforms of CCTV surveillance systems
DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to Autonomous and Assisted Driving

Autonomous and assisted driving are undoubtedly hot topics in computer vision. However, the driving task is extremely complex and a deep understanding of drivers' behavior is still lacking. Several researchers are now investigating the attention mechanism in order to define computational models for detecting salient and interesting objects in the scene. Nevertheless, most of these models only refer to bottom up visual saliency and are focused on still images. Instead, during the driving experience the temporal nature and peculiarity of the task influence the attention mechanisms, leading to the conclusion that real life driving data is mandatory. In this paper we propose a novel and publicly available dataset acquired during actual driving. Our dataset, composed by more than 500,000 frames, contains drivers' gaze fixations and their temporal integration providing task-specific saliency maps. Geo-referenced locations, driving speed and course complete the set of released data. To the best of our knowledge, this is the first publicly available dataset of this kind and can foster new discussions on better understanding, exploiting and reproducing the driver's attention process in the autonomous and assisted cars of future generations.
Cluster partitioning in image analysis classification: a genetic algorithm approach

A classification of data by using the genetic algorithm computational paradigm is proposed. The best data partition is defined to be the one minimizing the sum of Pythagorean distances between each datum in a cluster and the relative center of class or center of mass. Background is given, and the relevant genetic algorithm description is provided. The model for the genetic application is presented. Simulation results confirm genetic algorithms to be powerful tools for the solution of optimization problems.<<ETX>>
Exploiting cache in multimedia

The paper explores cache strategies for multimedia. Although many architectural improvements have been designed for multimedia, the cache structure and the standard caching policies of general-purpose processors exhibit poor performance in exploiting the 2D spatial locality typical of programs handling and processing images. We propose a novel caching approach, suitably tailored to the requirement of multimedia programs. Our proposal exploits hardware pre-fetching for allocating in cache, blocks of data that satisfy the 2D spatial locality requirements. Results refer to a benchmark suite of multimedia programs including MPEG decoding and image processing programs with different data dependency and access schemes to image data.
Probabilistic posture classification for Human-behavior analysis

Computer vision and ubiquitous multimedia access nowadays make feasible the development of a mostly automated system for human-behavior analysis. In this context, our proposal is to analyze human behaviors by classifying the posture of the monitored person and, consequently, detecting corresponding events and alarm situations, like a fall. To this aim, our approach can be divided in two phases: for each frame, the projection histograms (Haritaoglu et al., 1998) of each person are computed and compared with the probabilistic projection maps stored for each posture during the training phase; then, the obtained posture is further validated exploiting the information extracted by a tracking module in order to take into account the reliability of the classification of the first phase. Moreover, the tracking algorithm is used to handle occlusions, making the system particularly robust even in indoors environments. Extensive experimental results demonstrate a promising average accuracy of more than 95% in correctly classifying human postures, even in the case of challenging conditions.
Hierarchical Boundary-Aware Neural Encoder for Video Captioning

The use of Recurrent Neural Networks for video captioning has recently gained a lot of attention, since they can be used both to encode the input video and to generate the corresponding description. In this paper, we present a recurrent video encoding scheme which can discover and leverage the hierarchical structure of the video. Unlike the classical encoder-decoder approach, in which a video is encoded continuously by a recurrent layer, we propose a novel LSTM cell which can identify discontinuity points between frames or segments and modify the temporal connections of the encoding layer accordingly. We evaluate our approach on three large-scale datasets: the Montreal Video Annotation dataset, the MPII Movie Description dataset and the Microsoft Video Description Corpus. Experiments show that our approach can discover appropriate hierarchical representations of input videos and improve the state of the art results on movie description datasets.
On the usefulness of object shape coding with MPEG-4

This paper reports the results of an in-depth analysis of the degree of usefulness of object shape coding in video compression. In particular, MPEG-4 is used as reference standard. The influence of different coding parameters on the performance is deeply examined and discussions on the results are provided. Object shape coding is compared with classical (MPEG-2) frame-based coding both at an objective level (by comparing PSNR/quality and bitrate/filesize) and at a subjective level (asking to a set of users to express their opinion on overall quality, cognitive effectiveness, and willingness to pay). In conclusion, this paper aims at answering to the question whether it is convenient to use object shape coding instead of frame-based coding or not.
Socially Constrained Structural Learning for Groups Detection in Crowd

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 (<inline-formula><tex-math notation="LaTeX">$G$</tex-math> <alternatives><inline-graphic xlink:type="simple" xlink:href="solera-ieq1-2470658.gif"/></alternatives></inline-formula>-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.
Towards the evaluation of reproducible robustness in tracking-by-detection

Conventional experiments on MTT are built upon the belief that fixing the detections to different trackers is sufficient to obtain a fair comparison. In this work we argue how the true behavior of a tracker is exposed when evaluated by varying the input detections rather than by fixing them. We propose a systematic and reproducible protocol and a MATLAB toolbox for generating synthetic data starting from ground truth detections, a proper set of metrics to understand and compare trackers peculiarities and respective visualization solutions.
A Semi-Automatic Video Annotation tool with MPEG-7 Content Collections

In this work, we present a general purpose system for hierarchical structural segmentation and automatic annotation of video clips, by means of standardized low level features. We propose to automatically extract some prototypes for each class with a context based intra-class clustering. Clips are annotated following the MPEG-7 standard directives to provide easier portability. Results of automatic annotation and semiautomatic metadata creation are provided
Embedded recurrent network for head pose estimation in car

An accurate and fast driver's head pose estimation is a rich source of information, in particular in the automotive context. Head pose is a key element for driver's behavior investigation, pose analysis, attention monitoring and also a useful component to improve the efficacy of Human-Car Interaction systems. In this paper, a Recurrent Neural Network is exploited to tackle the problem of driver head pose estimation, directly and only working on depth images to be more reliable in presence of varying or insufficient illumination. Experimental results, obtained from two public dataset, namely Biwi Kinect Head Pose and ICT-3DHP Database, prove the efficacy of the proposed method that overcomes state-of-art works. Besides, the entire system is implemented and tested on two embedded boards with real time performance.
People appearance tracing in video by spectral graph transduction

Following people in different video sources is a challenging task: variations in the type of camera, in the lighting conditions, in the scene settings (e.g. crowd or occlusions) and in the point of view must be accounted. In this paper we propose a system based only on appearance information that, disregarding temporal and spatial information, can be flexibly applied on both moving and static cameras. We exploit the joint use of transductive learning and spectral properties of graph Laplacians proposing a formulation of the people tracing problem as a semi-supervised classification. The knowledge encoded in two labeled input sets of positive and negative samples of the target person and the continuous spectral update of these models allow us to obtain a robust approach for people tracing in surveillance video sequences. Experiments on publicly available datasets show satisfactory results and exhibit a good robustness in dealing with short and long term occlusions.
Hands on the wheel: A Dataset for Driver Hand Detection and Tracking

The ability to detect, localize and track the hands is crucial in many applications requiring the understanding of the person behavior, attitude and interactions. In particular, this is true for the automotive context, in which hand analysis allows to predict preparatory movements for maneuvers or to investigate the driver's attention level. Moreover, due to the recent diffusion of cameras inside new car cockpits, it is feasible to use hand gestures to develop new Human-Car Interaction systems, more user-friendly and safe. In this paper, we propose a new dataset, called Turms, that consists of infrared images of driver's hands, collected from the back of the steering wheel, an innovative point of view. The Leap Motion device has been selected for the recordings, thanks to its stereo capabilities and the wide view-angle. Besides, we introduce a method to detect the presence and the location of driver's hands on the steering wheel, during driving activity tasks.
Iterative active querying for surveillance data retrieval in crime detection and forensics

Large sets of visual data are now available both, in real time and off line, at time of investigation in multimedia forensics, however passive querying systems often encounter difficulties in retrieving significant results. In this paper we propose an iterative active querying system for video surveillance and forensic applications based on the continuous interaction between the user and the system. The positive and negative user feedbacks are exploited as the input of a graph based transductive procedure for iteratively refining the initial query results. Experiments are shown using people trajectories and people appearance as distance metrics.
Computer vision system for in-house video surveillance

In-house video surveillance to control the safety of people living in domestic environments is considered. In this context, common problems and general purpose computer vision techniques are discussed and implemented in an integrated solution comprising a robust moving object detection module which is able to disregard shadows, a tracking module designed to handle large occlusions, and a posture detector. These factors, shadows, large occlusions and people's posture, are the key problems that are encountered with in-house surveillance systems. A distributed system with cameras installed in each room of a house can be used to provide full coverage of people's movements. Tracking is based on a probabilistic approach in which the appearance and probability of occlusions are computed for the current camera and warped in the next camera's view by positioning the cameras to disambiguate the occlusions. The application context is the emerging area of domotics (from the Latin word domus, meaning 'home', and informatics). In particular, indoor video surveillance, which makes it possible for elderly and disabled people to live with a sufficient degree of autonomy, via interaction with this new technology, which can be distributed in a house at affordable costs and with high reliability.