nils-herrmann/clean_scirepeval huggingface dataset:
Learning superpixel relations for supervised image segmentation

In this paper we propose to extend the well known graph cut segmentation framework by learning superpixel relations and use them to weight superpixel-to-superpixel edges in a superpixel graph. Adjacent superpixel-pairs are analyzed to build an object boundary model, able to discriminate between superpixel-pairs belonging to the same object or placed on the edge between the foreground object and the background. Several superpixel-pair features are investigated and exploited to build a non-linear SVM to learn object boundary appearance. The adoption of this modified graph cut enhances the performance of a previously proposed segmentation method on two publicly available datasets, reaching state-of-the-art results.
Steering wheel's angle tracking from camera-car

This paper proposes a general-purpose method to track the steering wheel's absolute angle by using a single camera vision system mounted inside the car. The approach is based on the modeling of the motion of the steering wheel, as it appears perspectively distorted by the point of view of the uncalibrated camera. We modified the Lucas-Kanade method for an approximately rotational motion model in order to provide the detection and tracking of significant features on the wheel. The experimental results are compared with ground-truth data obtained with different types of sensors.
Perspective and appearance context for people surveillance in open areas

Contextual information can be used both to reduce computations and to increase accuracy and this paper presents how it can be exploited for people surveillance in terms of perspective (i.e. weak scene calibration) and appearance of the objects of interest (i.e. relevance feedback on the training of a classifier). These techniques are applied to a pedestrian detector that exploits covariance descriptors through a LogitBoost classifier on Riemannian manifolds. The approach has been tested on a construction working site where complexity and dynamics are very high, making human detection a real challenge. The experimental results demonstrate the improvements achieved by the proposed approach.
Using computer vision techniques for dangerous situation detection in domotic applications

In this paper, we describe an integrated solution devised for In-House Video Surveillance, to control the safety of people living in a domestic environment, The system is composed of a robust moving object detection module, able to disregard shadows, a tracking module designed for large occlusion solution and of a posture detector, Shadows, large occIusions and deformable model of people are key features of in-house surveillance. Moreover, the requirements of high speed reaction to dangerous situations and the need to implement a reliable and low cost Me-viewing system, led io the
Event driven software architecture for multi-camera and distributed surveillance research systems

Surveillance of wide areas with several connected cameras integrated in the same automatic system is no more a chimera, but modular, scalable and flexible architectures are mandatory to manage them. This paper points out the main issues on the development of distributed surveillance systems and proposes an integrated framework particularly suitable for research purposes. As first, exploiting a computer architecture analogy, a three layer tracking system is proposed, which copes with the integration of both overlapping and non overlapping cameras. Then, a static service oriented architecture is adopted to collect and manage the plethora of high level modules, such as face detection and recognition, posture and action classification, and so on. Finally, the overall architecture is controlled by an event driven communication infrastructure, which assures the scalability and the flexibility of the system.
Detecting Moving Objects, Ghosts, and Shadows in Video Streams

Background subtraction methods are widely exploited for moving object detection in videos in many applications, such as traffic monitoring, human motion capture, and video surveillance. How to correctly and efficiently model and update the background model and how to deal with shadows are two of the most distinguishing and challenging aspects of such approaches. The article proposes a general-purpose method that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames. Pixels belonging to moving objects, ghosts, and shadows are processed differently in order to supply an object-based selective update. The proposed approach exploits color information for both background subtraction and shadow detection to improve object segmentation and background update. The approach proves fast, flexible, and precise in terms of both pixel accuracy and reactivity to background changes.
ViSOR: VIdeo Surveillance On-line Repository for annotation retrieval

The Imagelab Laboratory of the University of Modena and Reggio Emilia has designed a large video repository, aiming at containing annotated video surveillance footages. The Web interface, named ViSOR (video surveillance online repository), allows video browse, query by annotated concepts or by keywords, compressed preview, video download and upload. The repository contains metadata annotation, both manually annotated ground-truth data and automatically obtained outputs of a particular system. In such a manner, the users of the repository are able to perform validation tasks of their own algorithms as well as comparative activities.
Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model

Data-driven saliency has recently gained a lot of attention thanks to the use of convolutional neural networks for predicting gaze fixations. In this paper, we go beyond standard approaches to saliency prediction, in which gaze maps are computed with a feed-forward network, and present a novel model which can predict accurate saliency maps by incorporating neural attentive mechanisms. The core of our solution is a convolutional long short-term memory that focuses on the most salient regions of the input image to iteratively refine the predicted saliency map. In addition, to tackle the center bias typical of human eye fixations, our model can learn a set of prior maps generated with Gaussian functions. We show, through an extensive evaluation, that the proposed architecture outperforms the current state-of-the-art on public saliency prediction datasets. We further study the contribution of each key component to demonstrate their robustness on different scenarios.
An efficient Bayesian framework for on-line action recognition

On-line action recognition from a continuous stream of actions is still an open problem with fewer solutions proposed compared to time-segmented action recognition. The most challenging task is to classify the current action while finding its time boundaries at the same time. In this paper we propose an approach capable of performing on-line action segmentation and recognition by means of batteries of HMM taking into account all the possible time boundaries and action classes. A suitable Bayesian normalization is applied to make observation sequences of different length comparable and computational optimizations are introduce to achieve real-time performances. Results on a well known action dataset prove the efficacy of the proposed method.
Estimating Geospatial Trajectory of a Moving Camera

This paper proposes a novel method for estimating the geospatial trajectory of a moving camera. The proposed method uses a set of reference images with known GPS (global positioning system) locations to recover the trajectory of a moving camera using geometric constraints. The proposed method has three main steps. First, scale invariant features transform (SIFT) are detected and matched between the reference images and the video frames to calculate a weighted adjacency matrix (WAM) based on the number of SIFT matches. Second, using the estimated WAM, the maximum matching reference image is selected for the current video frame, which is then used to estimate the relative position (rotation and translation) of the video frame using the fundamental matrix constraint. The relative position is recovered up to a scale factor and a triangulation among the video frame and two reference images is performed to resolve the scale ambiguity. Third, an outlier rejection and trajectory smoothing (using b-spline) post processing step is employed. This is because the estimated camera locations may be noisy due to bad point correspondence or degenerate estimates of fundamental matrices. Results of recovering camera trajectory are reported for real sequences
DARPA benchmark image processing on SIMD parallel machines

The aim of the paper is to present a comparative analysis of the execution times of low-level vision algorithms on two different SIMD parallel machines. The set of algorithms is part of the DARPA Image Understanding benchmark, a widely-accepted platform for performance comparison of parallel systems in the field of computer vision. The considered computer architectures represent two opposite solutions in terms of granularity in approaching the SIMD paradigm, one with a coarse-grain array of floating-point processors and the other with a fine-grain array of single-bit processing elements. For these reasons, the set of algorithms was implemented on both systems taking into account machine specificities. Some insights into implementation issues and a comparative analysis of the assessed execution times are presented.
Image analysis and rule-based reasoning for a traffic monitoring system

The paper describes a system for detecting vehicles in urban traffic scenes in daytime and at night by means of image analysis and rule-based reasoning. The strength of the proposed approach is its formal separation between the low-level image processing modules (detecting moving vehicles under day and night light) and the high-level module, which provides a single framework for tracking vehicles in the scene. The image processing modules perform spatio-temporal analysis on moving templates in daytime images, and morphological analysis of headlight pairs in night images. The high-level module is designed as a forward chained production rule system, working on symbolic data, i.e. vehicles and their attributes, and exploiting a set of heuristic roles tuned to urban traffic conditions. The synergy between the artificial intelligence techniques of the high level and low-level image analysis techniques provides the system with flexibility and robustness.
A machine learning approach for human posture detection in domotics applications

This paper describes an approach for human posture classification that has been devised for indoor surveillance in domotic applications. The approach was initially inspired to a previous work of Haritaoglou et al. (1998) that uses histogram projections to classify people's posture. We modify and improve the generality of the approach by adding a machine learning phase in order to generate probability maps. A statistic classifier has then defined that compares the probability maps and the histogram profiles extracted from each of the moving people. The approach is very robust if the initial constraints are satisfied and exhibits a very low computational time so that it can be used to process live videos with standard platforms.
Generative adversarial models for people attribute recognition in surveillance

In this paper we propose a deep architecture for detecting people attributes (e.g. gender, race, clothing …) in surveillance contexts. Our proposal explicitly deal with poor resolution and occlusion issues that often occur in surveillance footages by enhancing the images by means of Deep Convolutional Generative Adversarial Networks (DCGAN). Experiments show that by combining both our Generative Reconstruction and Deep Attribute Classification Network we can effectively extract attributes even when resolution is poor and in presence of strong occlusions up to 80% of the whole person figure.
A multi-stage pedestrian detection using monolithic classifiers

Despite the many efforts in finding effective feature sets or accurate classifiers for people detection, few works have addressed ways for reducing the computational burden introduced by the sliding window paradigm. This paper proposes a multi-stage procedure for refining the search for pedestrians using the HOG features and the monolithic SVM classifier. The multi-stage procedure is based on particle-based estimation of pdfs and exploits the margin provided by the classifier to draw more particles on the areas where the classifier's response is higher. This iterative algorithm achieves the same accuracy than sliding window using less particles (and thus being more efficient) and, conversely, is more accurate when configured to work at the same computational load. Experimental results on publicly available datasets demonstrate that this method, previously proposed for boosted classifiers only, can be successfully applied to monolithic classifiers.
Processing of variable size images on a cellular array: Performance analysis with the Abingdon Cross Benchmark

Handling a continuous flow of variable size images is a requirement for real time computer vision machines. A modular system based on a small size SIMD cellular array of 1-bit processing elements has been developed with this goal in mind and it is now evaluated against the Abingdon Cross Benchmark specifications. The benchmark tests the combination of algorithms and architecture and generates a quality factor expressed as the ratio of the image lateral size and the processing time. The examined machine supports an efficient means to automatically partition, process and reconstruct images larger than the array size. The authors briefly describe the system, discuss the selected algorithms and present performance results and estimates for several system configurations.<<ETX>>
Fast gesture recognition with Multiple Stream Discrete HMMs on 3D skeletons

HMMs are widely used in action and gesture recognition due to their implementation simplicity, low computational requirement, scalability and high parallelism. They have worth performance even with a limited training set. All these characteristics are hard to find together in other even more accurate methods. In this paper, we propose a novel double-stage classification approach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and 3D skeleton joint data, able to reach high performances maintaining all advantages listed above. The approach allows both to quickly classify pre-segmented gestures (offline classification), and to perform temporal segmentation on streams of gestures (online classification) faster than real time. We test our system on three public datasets, MSRAction3D, UTKinect-Action and MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly created for Human Computer Interaction (HCI). We obtain state of the art performances on all of them.
A markerless approach for consistent action recognition in a multi-camera system

This paper presents a method for recognizing human actions in a multi-camera setup. The proposed method automatically extracts significant points on the human body, without the need of artificial markers. A sophisticated appearance-based tracking able to cope with occlusions is exploited to extract a probability map for each moving object. A segmentation technique based on mixture of Gaussians is then employed to extract and track significant points on this map, corresponding to significant regions on the human silhouette. The point tracking produces a set of 3D trajectories that are compared with other trajectories by means of global alignment and dynamic programming techniques. Preliminary experiments showed the potentiality of the proposed approach.
Posture classification in a multi-camera indoor environment

Posture classification is a key process for analyzing the people's behaviour. Computer vision techniques can be helpful in automating this process, but cluttered environments and consequent occlusions make this task often difficult. Different views provided by multiple cameras can be exploited to solve occlusions by warping known object appearance into the occluded view. To this aim, this paper describes an approach to posture classification based on projection histograms, reinforced by HMM for assuring temporal coherence of the posture. The single camera posture classification is then exploited in the multi-camera system to solve the cases in which the occlusions make the classification impossible. Experimental results of the classification from both the single camera and the multi-camera system are provided.
Class-Based Color Bag of Words for Fashion Retrieval

Color signatures, histograms and bag of colors are basic and effective strategies for describing the color content of images, for retrieving images by their color appearance or providing color annotation. In some domains, colors assume a specific meaning for users and the color-based classification and retrieval should mirror the initial suggestions given by users in the training set. For instance in fashion world, the names given to the dominant color of a garment or a dress reflect the fashion dictact and not an uniform division of the color space. In this paper we propose a general approach to implement color signature as a trained bag of words, defined on the basis of user defined color classes. The novel Class-based Color Bag of Words is a easy computable bag of words of color, constructed following an approach similar to the Median Cut algorithm, but biased by color distribution in the trained classes. Moreover, to dramatically reduce the computational effort we propose 3D integral histograms, a 3D extension of integral images, easily extensible for many histogram-based signature in 3D color space. Several comparisons in large fashion datasets confirm the discriminant power of this signature.
Real-time detection of moving vehicles

Computer vision-based traffic flow monitoring is of major importance for enforcing traffic management policies. Information such as the number of vehicles passing on a road per time unit, or vehicles' turning rates at intersections are exploited by traffic management policies to supervise traffic-light timings. Computer vision-based traffic flow monitoring requires extraction of moving vehicles from traffic scenes in real time. To accomplish this task, efficient algorithms must be used and effective, low-cost hardware implementation must be pursued. This paper first describes the algorithms used in the VTTS (vehicular traffic tracking system) to achieve segmentation of moving vehicles. Then, hardware implementation on a re-programmable FPGA-based board is described in detail.
Classification of Affective Data to Evaluate the Level Design in a Role-Playing Videogame

This paper presents a novel approach to evaluate game level design strategies, applied to role playing games. Following a set of well defined guidelines, two game levels were designed for Neverwinter Nights 2 to manipulate particular emotions like boredom or flow, and tested by 13 subjects wearing a brain computer interface helmet. A set of features was extracted from the affective data logs and used to classify different parts of the gaming sessions, to verify the correspondence of the original level aims and the effective results on people emotions. The very interesting correlations observed, suggest that the technique is extensible to other similar evaluation tasks.
Auto-iris compensation for traffic surveillance systems

This paper addresses auto-iris compensation. Auto-iris can be really troublesome for motion detection and tracking techniques based on background or frame differencing, since it can change quickly the average intensity of the current frame. To cope with this, we introduced a two-step auto-iris compensation approach in our traffic monitoring system. First, the auto-iris detection is based on the computation of the average of the luminance difference obtained by background suppression. Then, if an auto-iris is detected, the compensation phase is started. In this phase, the auto-iris' behaviour is empirically modelled and, thus, compensated. Experimental results demonstrate the accuracy of the proposed approach, with both quantitative measures and visual analysis.
Object and event detection for semantic annotation and transcoding

Video annotation provides a suitable way to describe, organize, and index stored videos. On the other hand, transcoding aims at adapting content to the user/client capabilities and requirements. Both cues are now mandatory, given the tremendous demand of multimedia access from remote clients, in particular nowadays that new terminals with limited resources (PDAs, HCCs, Smart phones) have access to the network. In this paper we propose a unified framework to define event-based and object-based semantic extraction from video to provide both semantic video annotation for video stored and semantic on-line transcoding from live cameras. Two case studies (highlights' extraction from soccer videos for the annotation and people behavior detection in domotic application for transcoding) and corresponding experimental results are reported.
Sensing floors for privacy-compliant surveillance of wide areas

Surveillance systems can really benefit from the integration of multiple and heterogeneous sensors. In this paper we describe an innovative sensing floor. Thanks to its low cost and ease of installation, the floor is suitable for both private and public environments, from narrow zones to wide areas. The floor is made adding a sensing layer below commercial floating tiles. The sensor is scalable, reliable, and completely invisible to the users. The temporal and spatial resolutions of the data are high enough to identify the presence of people, to recognize their behavior and to detect events in a privacy compliant way. Experimental results on a real prototype implementation confirm the potentiality of the framework.
Integrate tool for online analysis and offline mining of people trajectories

In the past literature, online alarm-based video-surveillance and offline forensic-based data mining systems are often treated separately, even from different scientific communities. However, the founding techniques are almost the same and, despite some examples in commercial systems, the cases on which an integrated approach is followed are limited. For this reason, this study describes an integrated tool capable of putting together these two subsystems in an effective way. Despite its generality, the proposal is here reported in the case of people trajectory analysis, both in real time and offline. Trajectories are modelled based on either their spatial location or their shape, and proper similarity measures are proposed. Special solutions to meet real-time requirements in both cases are also presented and the trade-off between efficiency and efficacy is analysed by comparing when using a statistical model and when not. Examples of results in large datasets acquired in the University campus are reported as preliminary evaluation of the system.
Personalized Egocentric Video Summarization of Cultural Tour on User Preferences Input

In this paper, we propose a new method for customized summarization of egocentric videos according to specific user preferences, so that different users can extract different summaries from the same stream. Our approach, tailored on a cultural heritage scenario, relies on creating a short synopsis of the original video focused on key shots, in which concepts relevant to user preferences can be visually detected and the chronological flow of the original video is preserved. Moreover, we release a new dataset, composed of egocentric streams taken in uncontrolled scenarios, capturing tourists cultural visits in six art cities, with geolocalization information. Our experimental results show that the proposed approach is able to leverage user's preferences with an accent on storyline chronological flow and on visual smoothness.