nils-herrmann/clean_scirepeval huggingface dataset:
Recognition of tennis strokes using key postures

In this paper we describe an approach for automatic recognition of tennis strokes using a single low cost camera. Professional tennis is played at high speed so the ability to classify tennis strokes on camera is hindered by the rapid movement of the players. We have developed an accurate recognition system which can automatically index tennis strokes from video footage. We aim to evolve this system so that meta data, such as time codes and descriptions of the strokes played, can be automatically indexed for a training session or a match. This level of indexing would provide an excellent foundation for the development of next generation sports coaching systems. The aim of this paper is to coarsely classify the main stokes played in tennis, i.e. a serve, forehand or backhand. The proposed approach is evaluated against a real-world dataset, obtained from elite players in a competitive training match.
3D Human Gait Reconstruction and Monitoring Using Body-Worn Inertial Sensors and Kinematic Modeling

In this paper, we present a novel low-cost computationally efficient method to accurately assess human gait by monitoring the 3D trajectory of the lower limb, both left and right legs outside the lab in any unconstrained environment. Our method utilizes a network of miniaturized wireless inertial sensors, coupled with a suite of real-time analysis algorithms and can operate in any unconstrained environment. First, we adopt a modified computationally efficient, highly accurate, and near real-time gradient descent algorithm to compute the direction of the gyroscope measurement error as a quaternion derivative in order to obtain the 3D orientation of each of the six segments. Second, by utilizing 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 left and right feet during functional phases of a gait cycle. Third, by setting the foot segment as the root node we calculate the 3D orientation and position of the other two segments as well as the left and right ankle, knee, and hip joints. We then employ a customized kinematic model adjustment technique to ensure that the motion is coherent with human biomechanical behavior of the leg. Pearson's correlation coefficient (r) and significant difference test results (F) were used to quantify the relationship between the calculated and measured movements for all joints in the sagittal plane. The correlation between the calculated and the reference was found to have similar trends for all six joints (r > 0.94, p <; 0.005).
Irish Sign Language Recognition Using Principal Component Analysis and Convolutional Neural Networks

Hand-shape recognition is an important problem in computer vision with significant societal impact. In this work, we introduce a new image dataset for Irish Sign Language (ISL) recognition and we compare between two recognition approaches. The dataset was collected by filming human subjects performing ISL hand-shapes and movements. Then, we extracted frames from the videos. This produced a total of 52,688 images for the 23 common hand- shapes from ISL. Afterwards, we filter the redundant images with an iterative image selection process that selects the images which keep the dataset diverse. For classification, we use Principal Component Analysis (PCA) with with K- Nearest Neighbours (k-NN) and Convolutional Neural Networks (CNN). We obtain a recognition accuracy of 0.95 for our PCA model and 0.99 for our CNN model. We show that image blurring improves PCA results to 0.98. In addition, we compare times for classification.
Architecture and challenges of maintaining a large-scale, context-aware human digital memory

Recent advances in digital multimedia capture, transmission and storage has led us close to realising the MEMEX vision, in which much of an individual's personal information can be stored for retrieval at a later stage. These personal archives (human digital memories (HDMs)) are constructed from a wide range of data sources across various media types. In this paper we examine the challenges of building, organising, browsing, searching and recommending from large (multi-million item per annum) visual HDMs. We present our findings based on an extensive period of HDM generation (2 years and 2 million images), illustrating the challenges of and architectural requirements for managing such archives.
Low-Energy Symmetric Key Distribution in Wireless Sensor Networks

In this work, a scheme for key distribution and network access in a Wireless Sensor Network (WSN) that utilizes Identity-Based Cryptography (IBC) is presented. The scheme is analyzed on the ARM920T processor and measurements were taken for the runtime and energy of its components. It was found that the Tate pairing component of the scheme consumes significant amounts of energy, and so should be ported to hardware. An accelerator was implemented in 65 nm Complementary Metal Oxide Silicon (CMOS) technology and area, timing, and energy figures have been obtained for the design. Results indicate that a hardware implementation of IBC would meet the strict energy constraint required of a wireless sensor network node.
A generic news story segmentation system and its evaluation

The paper presents an approach to segmenting broadcast TV news programmes automatically into individual news stories. We first segment the programme into individual shots, and then a number of analysis tools are run on the programme to extract features to represent each shot. The results of these feature extraction tools are then combined using a support vector machine trained to detect anchorperson shots. A news broadcast can then be segmented into individual stories based on the location of the anchorperson shots within the programme. We use one generic system to segment programmes from two different broadcasters, illustrating the robustness of our feature extraction process to the production styles of different broadcasters.
User-interface to a CCTV video search system

The proliferation of CCTV surveillance systems creates a problem of how to effectively navigate and search the resulting video archive, in a variety of security scenarios. We are concerned here with a situation where a searcher must locate all occurrences of a given person or object within a specified timeframe and with constraints on which camera(s) footage is valid to search. Conventional approaches based on browsing time/camera based combinations are inadequate. We advocate using automatically detected video objects as a basis for search, linking and browsing. In this paper we present a system under development based on users interacting with detected video objects. We outline the suite of technologies needed to achieve such a system and for each we describe where we are in terms of realizing those technologies. We also present a system interface to this system, designed with user needs and user tasks in mind.
User-Feedback on a Feature-Rich Photo Organiser

As the proliferation of digital photography increases, the software used to manage our increasingly large collections of digital photos becomes ever more important. In this paper we present the findings of a study which investigates how people view and interact with a set of photo management features. Specifically, a group of users are set the task of managing their own photos using a system built to encompass a wide range of photo organization functionality. They are then quizzed about their experiences in terms of their feature preferences, the general usability of the system, and other suggestions/requirements. Given these opinions, a basic estimation is then formed on what they like/dislike about the various aspects of the system, towards obtaining a more learned understanding of how we may develop a photo organizer that is optimal in terms of user satisfaction.
Action recognition in video using a spatial-temporal graph-based feature representation

We propose a video graph based human action recognition framework. Given an input video sequence, we extract spatio-temporal local features and construct a video graph to incorporate appearance and motion constraints to reflect the spatio-temporal dependencies among features. them. In particular, we extend a popular dbscan density-based clustering algorithm to form an intuitive video graph. During training, we estimate a linear SVM classifier using the standard Bag-of-words method. During classification, we apply Graph-Cut optimization to find the most frequent action label in the constructed graph and assign this label to the test video sequence. The proposed approach achieves state-of-the-art performance with standard human action recognition benchmarks, namely KTH and UCF-sports datasets and competitive results for the Hollywood (HOHA) dataset.
Vision-based analysis of pedestrian traffic data

Reducing traffic congestion has become a major issue within urban environments. Traditional approaches, such as increasing road sizes, may prove impossible in certain scenarios, such as city centres, or ineffectual if current predictions of large growth in world traffic volumes hold true. An alternative approach lies with increasing the management efficiency of pre-existing infrastructure and public transport systems through the use of intelligent transportation systems (ITS). In this paper, we focus on the requirement of obtaining robust pedestrian traffic flow data within these areas. We propose the use of a flexible and robust stereo-vision pedestrian detection and tracking approach as a basis for obtaining this information. Given this framework, we propose the use of a pedestrian indexing scheme and a suite of tools, which facilitates the declaration of user-defined pedestrian events or requests for specific statistical traffic flow data. The detection of the required events or the constant flow of statistical information can be incorporated into a variety of ITS solutions for applications in traffic management, public transport systems and urban planning.
TREAT: Terse Rapid Edge-Anchored Tracklets

Fast computation, efficient memory storage, and performance on par with standard state-of-the-art descriptors make binary descriptors a convenient tool for many computer vision applications. However their development is mostly tailored for static images. To respond to this limitation, we introduce TREAT (Terse Rapid Edge-Anchored Tracklets), a new binary detector and descriptor, based on tracklets. It harnesses moving edge maps to perform efficient feature detection, tracking, and description at low computational cost. Experimental results on 3 different public datasets demonstrate improved performance over other popular binary features. These experiments also provide a basis for benchmarking the performance of binary descriptors in video-based applications.
A combined audio-visual contribution to event detection in field sports broadcast video. Case study: Gaelic football

In this paper we propose novel, audio-visual analysis techniques for event detection in broadcast TV sports video content. The scope of the design is constrained to the specialized domain of 'field sport', and specifically, Gaelic Football is presented as an experimental case study. We show that a combination of speech-band energy tracking in the audio domain, coupled with colour dominance pattern recognition in the video domain, provides a useful contribution to event detection for broadcast Gaelic Football matches. It is projected that, any conclusions made therein may be extended such that they function on sports content of a similar nature such as American Football, Australian Rules, Rugby Union etc.
The AXES-lite video search engine

The aim of AXES is to develop tools that provide various types of users with new engaging ways to interact with audiovisual libraries, helping them discover, browse, navigate, search, and enrich archives. This paper describes the initial (lite) version of the AXES search engine, which is targeted at professional users such as media professionals and archivists. We describe the overall system design, the user interface, and the results of our experiments at TRECVid 2011.
Breathing Feedback System with Wearable Textile Sensors

Breathing exercises form an essential part of the treatment for respiratory illnesses such as cystic fibrosis. Ideally these exercises should be performed on a daily basis. This paper presents an interactive system using a wearable textile sensor to monitor breathing patterns. A graphical user interface provides visual real-time feedback to patients. The aim of the system is to encourage the correct performance of prescribed breathing exercises by monitoring the rate and the depth of breathing. The system is straight forward to use, low-cost and can be installed easily within a clinical setting or in the home. Monitoring the user with a wearable sensor gives real-time feedback to the user as they perform the exercise, allowing them to perform the exercises independently. There is also potential for remote monitoring where the user’s overall performance over time can be assessed by a clinician.
Detector adaptation by maximising agreement between independent data sources

Traditional methods for creating classifiers have two main disadvantages. Firstly, it is time consuming to acquire, or manually annotate, the training collection. Secondly, the data on which the classifier is trained may be over-generalised or too specific. This paper presents our investigations into overcoming both of these drawbacks simultaneously, by providing example applications where two data sources train each other. This removes both the need for supervised annotation or feedback, and allows rapid adaptation of the classifier to different data. Two applications are presented: one using thermal infrared and visual imagery to robustly learn changing skin models, and another using changes in saturation and luminance to learn shadow appearance parameters.
Investigating older and younger peoples' motivations for lifelogging with wearable cameras

People have a natural tendency to collect things about themselves, their experiences and their shared experiences with people important to them, especially family. Similar to traditional objects such as photographs, lifelogs have been shown to support reminiscence. A lifelog is a digital archive of a person's experiences and activities and lifelog devices such as wearable cameras can automatically and continuously record events throughout a whole day. We were interested in investigating what would motivate people to lifelog. Due to the importance of shared family reminiscence between family members we focused our study on comparing shared or personal motivations with ten older and ten younger family members. We found from our results that both older and younger adults were more likely to lifelog for the purposes of information sharing and that reviewing lifelog images supported family reminiscence, reflection and story-telling. Based on these findings, recommendations are made for the design of a novel intergenerational family lifelog system.
Automatically detecting asymmetric running using time and frequency domain features

Human motion analysis technologies have been widely employed to identify injury determining factors and provide objective and quantitative feedback to athletes to help prevent injury. However, most of these technologies are: expensive, restricted to laboratory environments, and can require significant post processing. This reduces their ecological validity, adoption and usefulness. In this paper, we present a novel wearable inertial sensor framework to accurately distinguish between symmetrical and asymmetrical running patterns in an unconstrained environment. The framework can automatically classify symmetry/asymmetry using Short Time Fourier Transform (STFT) and other time domain features in conjunction with a customized Random Forest classifier. The accuracy of the designed framework is up to 94% using 3-D accelerometer and 3-D gyroscope data from a sensor node attached on the upper back of a subject. The upper back inertial sensors data were then down-sampled by a factor of 4 to simulate utilizing low-cost inertial sensors whilst also facilitating a decrease of the computational cost to achieve near real-time application. We conclude that the proposed framework can potentially pave the way for employing low-cost sensors, such as those used in smartphones, attached on the upper back to provide injury related and performance feedback in real-time in unconstrained environments.
Improved graph cut segmentation by learning a contrast model on the fly

This paper describes an extension to the graph cut interactive image segmentation algorithm based on a novel approach to addressing the well known small cut problem. The approach uses a generative contrast model to weight interaction potentials. The model attempts to capture the expected changes in color between adjacent pixels in the unlabeled area of the image using the adjacent pixels in the user interactions as training data. We compare our approach to the standard graph cuts algorithm and show that the contrast model allows a user to achieve a more accurate segmentation with fewer interactions. We additionally introduce a variant of the approach based on superpixels that further enhances performance but reduces computational complexity to ensure instant feedback for optimal user experience.
Action recognition based on sparse motion trajectories

We present a method that extracts effective features in videos for human action recognition. The proposed method analyses the 3D volumes along the sparse motion trajectories of a set of interest points from the video scene. To represent human actions, we generate a Bag-of-Features (BoF) model based on extracted features, and finally a support vector machine is used to classify human activities. Evaluation shows that the proposed features are discriminative and computationally efficient. Our method achieves state-of-the-art performance with the standard human action recognition benchmarks, namely KTH and Weizmann datasets.
Team behavior analysis in sports using the Poisson equation

We propose a novel physics-based model for analysing team players' positions and movements on a sports playing field. The goal is to detect for each frame the region with the highest population of a given team's players and the region towards which the team is moving as they press for territorial advancement, termed the region of intent. Given the positions of team players from a plan view of the playing field at any given time, we solve a particular Poisson equation to generate a smooth distribution. The proposed distribution provides the likelihood of a point to be occupied by players so that more highly populated regions can be detected by appropriate thresholding. Computing the proposed distribution for each frame provides a sequence of distributions, which we process to detect the region of intent at any time during the game. Our model is evaluated on a field hockey dataset, and results show that the proposed approach can provide effective features that could be used to generate team statistics useful for performance evaluation or broadcasting purposes.
Blending real with virtual in 3DLife

Part of 3DLife's major goal to bring the 3D media Internet to life, concerns the development and wide-spread distribution of online tele-immersive (TI) virtual environments. As the techniques powering challenging tasks for user reconstruction and activity tracking within a virtual environment are maturing, along with consumer-grade availability of specialized hardware, this paper focuses on the simple practices used to make real-time tele-immersion within a networked virtual world a reality.
Visualisation of tennis swings for coaching

As a proficient tennis swing is a key element of success in tennis, many amateur tennis players spend a considerable amount of time and effort perfecting their tennis stroke mechanics, hoping to create more accuracy, consistency and power in their swing. In order to achieve these aims effectively a number of independent aspects of technique need to be addressed, including forming a correct racket grip, shot timing, body orientation and precise follow-through. Outside of a one-to-one coaching scenario, where constant professional feedback on technique can be provided, keeping all aspects of technique in mind can overwhelm amateur players. In this work, we have developed a set of visualisation tools to augment the development of amateur tennis players between dedicated one-to-one coaching sessions in the area of technique, timing and body posture. Our approach temporally aligns an amateur player's swing dynamics with that of an elite athlete, allowing direct technique comparison using augmented reality techniques.
Rhythm detection for speech-music discrimination in MPEG compressed domain

A novel approach to speech-music discrimination based on rhythm (or beat) detection is introduced. Rhythmic pulses are detected by applying a long-term autocorrelation method on band-passed signals. This approach is combined with another, in which the features describe the energy peaks of the signal. The discriminator uses just three features that are computed from data directly taken from an MPEG-1 bitstream. The discriminator was tested on more than 3 hours of audio data. Average recognition rate is 97.7%.
Multispectral Object Segmentation and Retrieval in Surveillance Video

This paper describes a system for object segmentation and feature extraction for surveillance video. Segmentation is performed by a dynamic vision system that fuses information from thermal infrared video with standard CCTV video in order to detect and track objects. Separate background modelling in each modality and dynamic mutual information based thresholding are used to provide initial foreground candidates for tracking. The belief in the validity of these candidates is ascertained using knowledge of foreground pixels and temporal linking of candidates. The transferable belief model is used to combine these sources of information and segment objects. Extracted objects are subsequently tracked using adaptive thermo-visual appearance models. In order to facilitate search and classification of objects in large archives, retrieval features from both modalities are extracted for tracked objects. Overall system performance is demonstrated in a simple retrieval scenario.
An Improved Spatiogram Similarity Measure for Robust Object Localisation

Spatiograms were introduced as a generalisation of the commonly used histogram, providing the flexibility of adding spatial context information to the feature distribution information of a histogram. The originally proposed spatiogram comparison measure has significant disadvantages that we detail here. We propose an improved measure based on deriving the Bhattacharyya coefficient for an infinite number of spatial-feature bins. Its advantages over the previous measure and over histogram-based matching are demonstrated in object tracking scenarios.