nils-herrmann/clean_scirepeval huggingface dataset:
A framework for comprehensive analysis of a swing in sports using low-cost inertial sensors

We present a novel framework to monitor the three-dimensional trajectory (orientation and position) of a golf swing using miniaturized inertial sensors. Firstly we employed a highly accurate and computationally efficient revised gradient descent algorithm to obtain the orientation of a golf club. Secondly, we designed a series of digital filters to determine the backward and forward segments of the swing, enabling us to calculate drift-free linear velocity along with the relative 3D position of the golf club during the entire swing. Finally, the calculated motion trajectory was verified against a ground truth VICON system using Iterative Closest Point (ICP) in conjunction with Principal Component Analysis (PCA). The computationally efficient framework present here achieves a high level of accuracy (r = 0.9885, p <; 0.0001) for such a low-cost system. This framework can be utilized for reliable movement technique evaluation and can provide near real-time feedback for athletes in various unconstrained environments. It is envisaged that the proposed framework is applicable to other racket based sports (e.g. tennis, cricket and hurling).
Multiple image view synthesis for free viewpoint video applications

Interactive audio-visual (AV) applications such as free viewpoint video (FVV) aim to enable unrestricted spatio-temporal navigation within multiple camera environments. Current virtual viewpoint view synthesis solutions for FVV are either purely image-based implying large information redundancy; or involve reconstructing complex 3D models of the scene. In this paper we present a new multiple image view synthesis algorithm that only requires camera parameters and disparity maps. The multi-view synthesis (MVS) approach can be used in any multi-camera environment and is scalable as virtual views can be created given 1 to N of the available video inputs, providing a means to gracefully handle scenarios where camera inputs decrease or increase over time. The algorithm identifies and selects only the best quality surface areas from available reference images, thereby reducing perceptual errors in virtual view reconstruction. Experimental results are presented and verified using both objective (PSNR) and subjective comparisons.
Abnormal crowd behavior detection using novel optical flow-based features

In this paper, we propose a novel optical flow based features for abnormal crowd behaviour detection. The proposed feature is mainly based on the angle difference computed between the optical flow vectors in the current frame and in the previous frame at each pixel location. The angle difference information is also combined with the optical flow magnitude to produce new, effective and direction invariant event features. A one-class SVM is utilized to learn normal crowd behavior. If a test sample deviates significantly from the normal behavior, it is detected as abnormal crowd behavior. Although there are many optical flow based features for crowd behaviour analysis, this is the first time the angle difference between optical flow vectors in the current frame and in the previous frame is considered as a anomaly feature. Evaluations on UMN and PETS2009 datasets show that the proposed method performs competitive results compared to the state-of-the-art methods.
Pre-Classification for Automatic Image Orientation

In this paper, we propose a novel method for automatic orientation of digital images. The approach is based on exploiting the properties of local statistics of natural scenes. In this way, we address some of the difficulties encountered in previous works in this area. The main contribution of this paper is to introduce a pre-classification step into carefully defined categories in order to simplify subsequent orientation detection. The proposed algorithm was tested on 9068 images and compared to existing state of the art in the area. Results show a significant improvement over previous works
Using Dempster-Shafer Theory to Fuse Multiple Information Sources in Region-Based Segmentation

This paper presents a new method for segmentation of images into large regions that reflect the real world objects present in a scene. It explores the feasibility of utilizing spatial configuration of regions and their geometric properties (the so-called syntactic visual features by C. Ferran Bennstrom and JR Casas (2004)) for improving the correspondence of segmentation results produced by the well-known recursive shortest spanning tree (RSST) algorithm by O.J. Morris et al. (1986) to semantic objects present in the scene. The main contribution of this paper is a novel framework for integration of evidence from multiple sources with the region merging process based on the Dempster-Shafer (DS) theory by P. Smets (1988) that allows integration of sources providing evidence with different accuracy and reliability. Extensive experiments indicate that the proposed solution limits formation of regions spanning more than one semantic object.
Event detection in field sports video using audio-visual features and a support vector Machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable.
A flexible ensemble-SVM for computer vision tasks

This paper presents an ensemble-SVM method that features a data selection mechanism with stochastic and deterministic properties, the use of extreme value theory for classifier calibration, and the introduction of random forest for classifier combination. We applied the proposed algorithm to 2 event recognition datasets and the PASCAL2007 object detection dataset and compared it to single SVM and common computer vision ensemble-SVM methods. Our algorithm outperforms its competitors and shows a considerable boost on datasets with a limited amount of outliers.
SaltiNet: Scan-Path Prediction on 360 Degree Images Using Saliency Volumes

We introduce SaltiNet, a deep neural network for scan-path prediction trained on 360-degree images. The model is based on a temporal-aware novel representation of saliency information named the saliency volume. The first part of the network consists of a model trained to generate saliency volumes, whose parameters are fit by back-propagation computed from a binary cross entropy (BCE) loss over downsampled versions of the saliency volumes. Sampling strategies over these volumes are used to generate scan-paths over the 360-degree images. Our experiments show the advantages of using saliency volumes, and how they can be used for related tasks. Our source code and trained models available at https://github.com/massens/saliency-360salient-2017.
Image-based vehicle indexing for a seaport transportation surveillance system

In this paper we describe and evaluate two methods underpinning a surveillance-based content management system, designed for monitoring and profiling freight-based vehicular traffic in a seaport environment. The ‘InSPeCT’ system captures video footage of passing vehicles, and uses tailored optical character recognition (OCR) to index the footage according to vehicle license-plates and freight codes. The system provides advanced search techniques for the efficient retrieval of records, where each captured vehicle is profiled according to captured imagery and its associated interpretations. Underpinning this system is a method for detecting the boundaries of individual transits within sustained traffic flow. Considering it desirable to extend the indexing functionality of the system beyond OCR, a colour-based vehicle indexing approach is proposed and evaluated. All evaluations take place in the context of a system deployed in a busy national seaport.
A multi-modal event detection system for river and coastal marine monitoring applications

This work is investigating the use of a multi-modal sensor network where visual sensors such as cameras and satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network.
Automatic camera selection for activity monitoring in a multi-camera system for tennis

In professional tennis training matches, the coach needs to be able to view play from the most appropriate angle in order to monitor players' activities. In this paper, we describe and evaluate a system for automatic camera selection from a network of synchronised cameras within a tennis sporting arena. This work combines synchronised video streams from multiple cameras into a single summary video suitable for critical review by both tennis players and coaches. Using an overhead camera view, our system automatically determines the 2D tennis-court calibration resulting in a mapping that relates a player's position in the overhead camera to their position and size in another camera view in the network. This allows the system to determine the appearance of a player in each of the other cameras and thereby choose the best view for each player via a novel technique. The video summaries are evaluated in end-user studies and shown to provide an efficient means of multi-stream visualisation for tennis player activity monitoring.
Enhancing Person Annotation for Personal Photo Management Applications

This paper addresses a sub-problem of the broad annotation problem, namely "person annotation", associated with personal digital photo management and investigates approaches to enhancing person annotation in personal photo management applications. We study a number of approaches to enhance the performance of semi-automatic person annotation using real-life personal photo collections as the test data. To this end, face and body-patch features are employed to describe the appearance of a person as a means to more effectively capture the identities of re-appearing people in personal photo archives. Experiments are carried out to identify a suitable initial annotation method, compare the performances of event-constrained person matching with global person matching, and the effect of the size of initial annotation on the overall performance of person annotation in real-life personal photo archives. The evaluation results, presented in terms of H-Hit rate figures, illustrate that using event-constrained person matching with event-based initial annotation proves to be a better performing approach than global person matching for person annotation in personal photo archives.Results also clearly demonstrate the nature of compromise that needs to be made when annotating large photo collections in terms of accuracy against user-interaction.
Object detection and tracking using an EM-based motion estimation and segmentation framework

This paper addresses the segmentation and subsequent tracking of the moving objects within a video sequence. The approach is to jointly produce segmentations and motion parameters which minimise the interframe coding rate of the video sequence. This minimisation is performed using a previously proposed framework based on the expectation-maximisation (EM) algorithm and the minimum description length (MDL) estimate. The paper concentrates on extensions of this framework which have been incorporated to ensure spatial and temporal coherence in the output segmentation sequence. The work has been conducted considering video coding applications and in particular, the MPEG-4 standardisation effort.
The acetoolbox: low-level audiovisual feature extraction for retrieval and classification

In this paper we present an overview of a software platform that has been developed within the aceMedia project, termed the aceToolbox, that provides global and local lowlevel feature extraction from audio-visual content. The toolbox is based on the MPEG-7 experimental Model (XM), with extensions to provide descriptor extraction from arbitrarily shaped image segments, thereby supporting local descriptors reflecting real image content. We describe the architecture of the toolbox as well as providing an overview of the descriptors supported to date. We also briefly describe the segmentation algorithm provided. We then demonstrate the usefulness of the toolbox in the context of two different content processing scenarios: similarity-based retrieval in large collections and scene-level classification of still images.
Event detection based on generic characteristics of field-sports

In this paper, we propose a generic framework for event detection in broadcast video of multiple different field-sports. Features indicating significant events are selected, and robust detectors built. These features are rooted in generic characteristics common to all genres of field-sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested across multiple genres of field-sports including soccer, rugby, hockey and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable.
Action localization in video using a graph-based feature representation

We propose a new framework for human action localization in video sequences. The option to not only detect but also localize actions in surveillance video is crucial to improving system's ability to manage high volumes of CCTV. In the approach, the action localization task is formulated the maximum-path finding problem in the directed spatio-temporal video-graph. The graph is constructed on the top of frame and temporal-based low-level features. To localize actions in the video-graph, we apply a maximum-path algorithm to find the path in the graph that is considered to be the localized action in the video. The proposed approach achieves competitive performance with the J-HMDB and the UCF-Sports dataset.
SHREC’08 entry: Multi-view 3D retrieval using multi-scale contour representation

We describe in this paper a method for 3D shape indexing and retrieval that we apply on three data collections of the SHREC - SHape Retrieval Contest 2008: Stability on watertight, CAD and Generic 3D models. The method is based on a set of 2D multi-views after a pose and scale normalization of the models using Continuous PCA and the enclosing sphere. In all views we extract the models silhouettes and compare them pairwise. To compute the similitude measure we consider the external contour of the silhouettes, we extract their convexities and concavities at different scale levels and we build a multiscale representation. The pairs of contours are then compared by elastic matching achieved by using dynamic programming.
Inexpensive Fusion Methods for Enhancing Feature Detection

In this paper we present two fusion methods for the task of high-level feature detection in multimedia content. Successful approaches to high-level feature detection typically leverage the techniques learned from Machine Learning utilized through ensemble architectures to achieve strong performance. However these approaches whilst successful are computationally expensive, and depending on the task require the use of significant computational resources. We propose two fusion methods that aim to combine the output of an initial basic machine learning approach with a lower-quality information source in order to gain diversity in the classified results whilst only requiring modest computing resources.
A machine learning approach to determining tag relevance in geotagged Flickr imagery

We present a novel machine learning based approach to determining the semantic relevance of community contributed image annotations for the purposes of image retrieval. Current large scale community image retrieval systems typically rely on human annotated tags which are subjectively assigned and may not provide useful or semantically meaningful labels to the images. Homogeneous tags which fail to distinguish between are a common occurrence, which can lead to poor search effectiveness on this data. We described a method to improve text based image retrieval systems by eliminating generic or non relevant image tags. To classify tag relevance, we propose a novel feature set based on statistical information available for each tag within a collection of geotagged images harvested from Flickr. Using this feature set machine learning models are trained to classify the relevance of each tag to its associated image. The goal of this process is to allow for rich and accurate captioning of these images, with the objective of improving the accuracy of text based image retrieval systems. A thorough evaluation is carried out using a human annotated benchmark collection of Flickr tags.
Towards Automated Robust Vision-Based Surveillance

COST is one of the longest-running instruments funded by the EU RTD Framework Program. The members of the COST 292 action (see: http://www.cost292.org/) have a long tradition of involvement in WIAMIS, dating back to the establishment of this event. Since 2005, the action has been invited to organize a special session and this tradition was continued for WIAMIS 2008. This year, the special session focused on a subset of the broad research remit of the action. Specifically, the session targets research in the field of automated visual surveillance. This focus reflects a growing interest within COST 292 participants in this increasingly important area. The goal of this special session is to help bring together researchers working on this topic, both within COST 292 and beyond, with a view to stimulating future collaborative research in this area. The widely circulated call for papers targeted submissions on a broad range of topics covered by the above mentioned focus, including visual computation, event and activity modeling and analysis, multiple stream analysis, architectures, indexing and storage, coding and applications. All papers were peer reviewed by at least three reviewers drawn from a combination of the WIAMIS Technical Program Committee and a special Program Subcommittee set up specifically for this special session. Acceptance criteria considered overall quality as well as relevance to the topics of the special session. In the end, just 5 papers were accepted for oral presentation. The accepted papers address scene modeling, event detection, object recognition (human and non-human) and fusion of multiple complementary data sources.
Human gait monitoring using body-worn inertial sensors and kinematic modelling

In this paper, we present a low-cost computationally efficient method to accurately assess Gait by monitoring the 3D trajectory of the lower limb (i.e. 3 segments - foot, tibia and thigh, and 2 joints - ankle and knee). Our method utilises a network of miniaturized wireless inertial sensors, coupled with a suite of sophisticated real-time analysis algorithms and can operate in any unconstrained environment. Firstly, we adopt a modified computationally-efficient, highly accurate and realtime gradient descent algorithm to obtain the 3D orientation of each of the 3 segments. Secondly, by utilising the foot sensor, we successfully detect the stance phase of the human gait cycle, which allows us to obtain drift-free velocity and the 3D position of the foot during functional phases of a gait cycle (i.e. heel strike to heel strike). Thirdly, by setting the foot segment as the root node we calculate the 3D orientation and position of the other 2 segments as well as the ankle and knee joints. Finally, we employ a customised kinematic model adjustment technique to ensure that the motion is coherent with human biomechanical behaviour of the leg. Our method is low-cost, is robust to measurement drift and can accurately monitor human gait outside the lab in any unconstrained environment.
Analyzing Image-Text Relations for Semantic Media Adaptation and Personalization

Progress in semantic media adaptation and personalisation requires that we know more about how different media types, such as texts and images, work together in multimedia communication. To this end, we present our ongoing investigation into image-text relations. Our idea is that the ways in which the meanings of images and texts relate in multimodal documents, such as web pages, can be classified on the basis of low-level media features and that this classification should be an early processing step in systems targeting semantic multimedia analysis. In this paper we present the first empirical evidence that humans can predict something about the main theme of a text from an accompanying image, and that this prediction can be emulated by a machine via analysis of low-level image features. We close by discussing how these findings could impact on applications for news adaptation and personalisation, and how they may generalise to other kinds of multimodal documents and to applications for semantic media retrieval, browsing, adaptation and creation.
A Framework for Human-like Behavior in an immersive virtual world

Just as readers feel immersed when the story line adheres to their experiences, users will more easily feel immersed in a virtual environment if the behavior of the characters in that environment adheres to their expectations, based on their lifelong observations in the real world. This paper introduces a framework that allows authors to establish natural, human-like behavior, physical interaction and emotional engagement of characters living in a virtual environment. Represented by realistic virtual characters, this framework allows people to feel immersed in an Internet based virtual world in which they can meet and share experiences in a natural way as they can meet and share experiences in real life. Rather than just being visualized in a 3D space, the virtual characters (autonomous agents as well as avatars representing users) in the immersive environment facilitate social interaction and multi-party collaboration, mixing virtual with real.
Intelligent Middleware for Adaptive Sensing of Tennis Coaching Sessions

In professional tennis training matches, the coachneeds to be able to view play from the most appropriate anglein order to monitor players activities. In this paper, we presenta system which can adapt the operation of a series of camerasin order to maintain optimal system performance based on aset of wireless sensors. This setup is used as a testbed for anagent based intelligent middleware that can correlate data frommany different wired and wireless sensors and provide effectivein-situ decision making. The proposed solution is flexible enoughto allow the addition of new sensors and actuators. Within thissetup we also provide details of a case study for the embeddedcontrol of cameras through the use of Ubisense data.