nils-herrmann/clean_scirepeval huggingface dataset:
An advanced virtual dance performance evaluator

The ever increasing availability of high speed Internet access has led to a leap in technologies that support real-time realistic interaction between humans in online virtual environments. In the context of this work, we wish to realise the vision of an online dance studio where a dance class is to be provided by an expert dance teacher and to be delivered to online students via the web. In this paper we study some of the technical issues that need to be addressed in this challenging scenario. In particular, we describe an automatic dance analysis tool that would be used to evaluate a student's performance and provide him/her with meaningful feedback to aid improvement.
Optimising resource allocation for background modeling using algorithm switching

Background maintenance is a complex problem due to varying scene conditions. Typically, a single algorithm cannot handle the complex scene changes that occur in visual surveillance applications. Also complex background modelling techniques, for example mixture of Gaussians have a high computational and communication demand compared to simple techniques such as a uni-model background model or simple frame-differencing with an adaptive threshold. In this paper we present an algorithm switching approach that can handle multi-model background scenes. Starting with a less computational and less data intensive uni-model background subtraction algorithm the system switches to a complex multi-model background subtraction there by saving valuable software and hardware resources both in terms of power and computation time. Our results show this algorithm switching approach can be used to effectively handle various scene conditions encountered in real time surveillance systems with optimal use of system resources.
Fast Intra Prediction in the Transform Domain

The paper reports a new fast intra prediction algorithms based on separating the transformed coefficients of neighboring blocks. The prediction blocks are obtained from the transformed and quantized neighboring blocks that generate minimum distortion for each DC and AC coefficients. To obtain fast coding with comparable coding efficiency compared to H.264/AVC, we present the Full Block Search Prediction (FBSP) and the Edge Based Distance Prediction (EBDP). These are immune to both the intra prediction error and the drift propagation; in addition, do not require a low pass filtering named extrapolation and mode decisions to obtain a prediction block. Experimental results show that the use of transform coefficients greatly enhances the efficiency of intra prediction whilst keeping complexity low compared to H.264/AVC.
A multiscale representation method for nonrigid shapes with a single closed contour

In this paper, we discuss the criteria that should be satisfied by a descriptor for nonrigid shapes with a single closed contour. We then propose a shape representation method that fulfills these criteria. In the proposed approach, contour convexities and concavities at different scale levels are represented using a two-dimensional (2-D) matrix. The representation can be visualized as a 2-D surface, where "hills" and "valleys" represent contour convexities and concavities, respectively. The optimal matching of two shape representations is achieved using dynamic programming and a dissimilarity measure is defined based on this matching. The proposed algorithm is very efficient and invariant to several kinds of transformations including some articulations and modest occlusions. The retrieval performance of the approach is illustrated using the MPEG-7 shape database, which is one of the most complete shape databases currently available. Our experiments indicate that the proposed representation is well suited for object indexing and retrieval in large databases. Furthermore, the representation can be used as a starting point to obtain more compact descriptors.
Incorporating spatio-temporal mid-level features in a region segmentation algorithm for video sequences

Segmentation algorithms traditionally employ low-level features to divide images into different regions that show a certain degree of homogeneity. However, low-level features, spatial or temporal, are not always reliable when processing real-world video sequences, because of issues like illuminations or complex backgrounds. Furthermore, real world objects can be composed of different regions with heterogeneous features. Although the inclusion of motion can mitigate some of these effects, many problems are still present. This paper proposes the utilization of some spatio-temporal mid-level features that are related, on the one hand, to geometric properties of real objects and, on the other, to well-known motion patterns. Specifically, the proposed algorithm uses a mid-level module that controls the subsequent segmentation using these kinds of features. Some experiments and evaluations show that the inclusion of mid-level features can help to obtain perceptually more meaningful segmentations, thus resulting in regions that are closer to semantic concepts.
Low-Energy Finite Field Arithmetic Primitives for Implementing Security in Wireless Sensor Networks

In this paper we propose the use of identity based encryption (IBE) for ensuring a secure wireless sensor network. In this context we have implemented the arithmetic operations required for the most computationally expensive part of IBE, which is the Tate pairing, in 90 nm CMOS and obtained area, timing and energy figures for the designs. Initial results indicate that a hardware implementation of IBE would meet the strict energy constraint of a wireless sensor network node
SEAMLOC: Seamless Indoor Localization Based on Reduced Number of Calibration Points

Indoor localization based on ubiquitous WLAN has exhibited the capability of being a cheap and relatively precise technology and has been verified by many successful examples. Its performance is subject to change due to multipath propagation and changes in the environment (people, building layouts, antenna characteristics, etc.) which cannot be eliminated easily. An indoor localization and tracking algorithm is described which is based on the creation of a database of WLAN signal strengths at pre-chosen calibration points (CPs). The need for fewer CPs than in standard methods is due to use of a novel interpolation algorithm, based on the specification of robust, range and angle-dependent likelihood functions that describe the probability of a user being in the vicinity of each CP. The actual location of the user is estimated by solving a system of two non-linear equations with two unknowns derived for a pair of CPs. Different pairs of CPs can be chosen to make several estimates which then can be combined to increase the accuracy of the estimate. A variety of results are presented using challenging data collected in a typical office environment which demonstrate the accuracy that can be achieved with a reduced number of CPs.)) The method is compared to several competing localization methods and is shown to give superior results.
Complexity adaptation in H.264/AVC video coder for static cameras

H.264/AVC uses variable block size motion estimation (VBS-ME) to improve coding gain. However, its complexity is significant and fixed regardless of the required quality or of the scene characteristics. In this paper, we propose an adaptive complexity algorithm based on using the Walsh Hadamard Transform (WHT). VBS automatic partition and skip mode detection algorithms also are proposed. Experimental results show that 70% − 5% of the computation of H.264/AVC is required to achieve the same PSNR.
FPGA-based conformance testing and system prototyping of an MPEG-4 SA-DCT hardware accelerator

Two FPGA implementations of a shape adaptive discrete cosine transform (SA-DCT) accelerator are presented in this paper: one PCI-based and the other AMBA-based. The former is used for conformance testing with the MPEG-4 standard requirements. The latter is an alternative platform for system prototyping and has an architecture more representative of a mobile device. The proposed accelerator meets real time constraints on both platforms with a gate count of approximately 40k, and outperforms the optimised reference software implementation by 20times. It is estimated that the accelerator consumes 250mW on a Virtex-E FPGA and 79mW on a Virtex-II FPGA in the worst case scenario
Identification of moving objects in poor quality surveillance data

In a world of pervasive visual surveillance and fast computing there is a growing interest in automated surveillance analytics. Object classification can support existing event detection techniques by identifying objects present allowing confident prioritization of the detected events. In this paper we propose an effective object classification algorithm to distinguish between four classes that are important for outdoor surveillance applications: people, vehicles, animals and `other'. A challenging dataset that has been obtained from an industry partner from real deployments of poor quality cameras is used to evaluate the proposed approach. Frame differencing was found to be the most suitable approach to detect moving objects with Histogram of Oriented Gradients (HOG) the preferred choice to represent the objects. An SVM was used for classification. The results show that the proposed approach gives higher accuracy than a similar approach based on SIFT and bag words.
Identifying Different Settings in a Visual Diary

We describe an approach to identifying specific settings in large collections of photographs corresponding to a visual diary. An algorithm developed for setting detection should be capable of clustering images captured at the same real world locations (e.g. in the dining room at home, in front of the computer in the office, in the park, etc.). This requires the selection and implementation of suitable methods to identify visually similar backgrounds in images using their visual features. The goal of the work reported here is to automatically detect settings in images taken over a single week. We achieve this using scale invariant feature transform (SIFT) features and X-means clustering. In addition, we also explore how the use of location based metadata can aid this process.
Automatic Activity Classification and Movement Assessment During a Sports Training Session Using Wearable Inertial Sensors

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in an outdoor training environment. We firstly present a system that automatically classifies a large range of training activities using the Discrete Wavelet Transform (DWT) in conjunction with a Random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Secondly, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the thigh and shank of a subject, from which the flexion-extension knee angle is calculated. Finally, a curve shift registration technique is applied to both generate normative data and determine if a subject's movement technique differed to the normative data in order to identify potential injury related factors. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments.
Identification of sleep apnea events using discrete wavelet transform of respiration, ECG and accelerometer signals

Sleep apnea is a common sleep disorder in which patient sleep patterns are disrupted due to recurrent pauses in breathing or by instances of abnormally low breathing. Current gold standard tests for the detection of apnea events are costly and have the addition of long waiting times. This paper investigates the use of cheap and easy to use sensors for the identification of sleep apnea events. Combinations of respiration, electrocardiography (ECG) and acceleration signals were analysed. Results show that using features, formed using the discrete wavelet transform (DWT), from the ECG and acceleration signals provided the highest classification accuracy, with an F1 score of 0.914. However, the novel employment of just the accelerometer signal during classification provided a comparable F1 score of 0.879. By employing one or a combination of the analysed sensors a preliminary test for sleep apnea, prior to the requirement for gold standard testing, can be performed.
A low complexity hardware architecture for motion estimation

This paper tackles the problem of accelerating motion estimation for video processing. A novel architecture using binary data is proposed, which attempts to reduce power consumption. The solution exploits redundant operations in the sum of absolute differences (SAD) calculation, by a mechanism known as early termination. Further data redundancies are exploited by using a run length coding addressing scheme, where access to pixels which do not contribute to the final SAD value is minimised. By using these two techniques operations and memory accesses are reduced by 93.29% and 69.17% respectively relative to a systolic array implementation
Multi-modal sensor networks for more effective sensing in Irish coastal and freshwater environments

The world's oceans represent a vital resource to global economies and there exists huge economic opportunity that remains unexploited. However along with this huge potential there rests a responsibility into understanding the effects various developments may have on our natural ecosystem. This along with a variety of other issues necessitates a need for continuous and reliable monitoring of the marine and freshwater environment. The potential for innovative technology development for marine and freshwater monitoring and knowledge generation is huge and recent years have seen huge leaps forward in relation to the development of sensor technology for such purposes. However despite the advancements there are still a number of issues. In our research we advocate a multi-modal approach to create smarter more efficient monitoring networks, while enhancing the use of in-situ wireless sensor networks (WSNs). In particular we focus on the use of visual sensors, modelled outputs and context information to support a conventional in-situ wireless sensor network creating a multi-modal environmental monitoring network. Here we provide an overview of a selection of our work in relation to the use of visual sensing through networked cameras or satellite imagers in three very diverse test sites - a river catchment, a busy port and a coastal environment.
Shallow and Deep Convolutional Networks for Saliency Prediction

The prediction of salient areas in images has been traditionally addressed with hand-crafted features based on neuroscience principles. This paper, however, addresses the problem with a completely data-driven approach by training a convolutional neural network (convnet). The learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth. The recent publication of large datasets of saliency prediction has provided enough data to train end-to-end architectures that are both fast and accurate. Two designs are proposed: a shallow convnet trained from scratch, and a another deeper solution whose first three layers are adapted from another network trained for classification. To the authors' knowledge, these are the first end-to-end CNNs trained and tested for the purpose of saliency prediction.
Multi-sensor classification of tennis strokes

In this work, we investigate tennis stroke recognition using a single inertial measuring unit attached to a player's forearm during a competitive match. This paper evaluates the best approach for stroke detection using either accelerometers, gyroscopes or magnetometers, which are embedded into the inertial measuring unit. This work concludes what is the optimal training data set for stroke classification and proves that classifiers can perform well when tested on players who were not used to train the classifier. This work provides a significant step forward for our overall goal, which is to develop next generation sports coaching tools using both inertial and visual sensors in an instrumented indoor sporting environment.
Towards an optimised VLSI design algorithm for the constant matrix multiplication problem

The efficient design of multiplierless implementations of constant matrix multipliers is challenged by the huge solution search spaces even for small scale problems. Previous approaches tend to use hill-climbing algorithms risking sub-optimal results. The proposed algorithm avoids this by exploring parallel solutions. The computational complexity is tackled by modelling the problem in a format amenable to genetic programming and hardware acceleration. Results show an improvement on state of the art algorithms with future potential for even greater savings
Learning Midlevel Image Features for Natural Scene and Texture Classification

This paper deals with coding of natural scenes in order to extract semantic information. We present a new scheme to project natural scenes onto a basis in which each dimension encodes statistically independent information. Basis extraction is performed by independent component analysis (ICA) applied to image patches culled from natural scenes. The study of the resulting coding units (coding filters) extracted from well-chosen categories of images shows that they adapt and respond selectively to discriminant features in natural scenes. Given this basis, we define global and local image signatures relying on the maximal activity of filters on the input image. Locally, the construction of the signature takes into account the spatial distribution of the maximal responses within the image. We propose a criterion to reduce the size of the space of representation for faster computation. The proposed approach is tested in the context of texture classification (111 classes), as well as natural scenes classification (11 categories, 2037 images). Using a common protocol, the other commonly used descriptors have at most 47.7% accuracy on average while our method obtains performances of up to 63.8%. We show that this advantage does not depend on the size of the signature and demonstrate the efficiency of the proposed criterion to select ICA filters and reduce the dimension
Holistic features for real-time crowd behaviour anomaly detection

This paper presents a new approach to crowd behaviour anomaly detection that uses a set of efficiently computed, easily interpretable, scene-level holistic features. This low-dimensional descriptor combines two features from the literature: crowd collectiveness and crowd conflict, with two newly developed crowd features: mean motion speed and a new formulation of crowd density. Two different anomaly detection approaches are investigated using these features. When only normal training data is available we use a Gaussian Mixture Model (GMM) for outlier detection. When both normal and abnormal training data is available we use a Support Vector Machine (SVM) for binary classification. We evaluate on two crowd behaviour anomaly detection datasets, achieving both state-of-the-art classification performance on the violent-flows dataset as well as better than real-time processing performance (40 frames per second).
A comparison between end-to-end approaches and feature extraction based approaches for Sign Language recognition

In this paper we use a new image dataset for Irish Sign Language (ISL) and we compare different approaches for recognition. We perform experiments and report comparative accuracy and timing. We perform tests over blurred images and compare results with non-blurred images. For classification, we use end-to-end approaches, such as Convolutional Neural Networks (CNN) and feature based extraction approaches, such as Principal Component Analysis (PCA) followed by different classifiers, e.g. multilayer perceptron (MLP). We obtain a recognition accuracy over 99% for both approaches. In addition, we report different ways to split the training and testing dataset, one being iterative and the other randomly selected.
PhD forum: Investigating the performance of a multi-modal approach to unusual event detection

In this paper, we investigate the parameters underpinning our previously presented system for detecting unusual events in surveillance applications . The system identifies anomalous events using an unsupervised data-driven approach. During a training period, typical activities within a surveilled environment are modeled using multi-modal sensor readings. Significant deviations from the established model of regular activity can then be flagged as anomalous at run-time. Using this approach, the system can be deployed and automatically adapt for use in any environment without any manual adjustment. Experiments carried out on two days of audio-visual data were performed and evaluated using a manually annotated ground-truth. We investigate sensor fusion and quantitatively evaluate the performance gains over single modality models. We also investigate different formulations of our cluster-based model of usual scenes as well as the impact of dynamic thresholding on identifying anomalous events. Experimental results are promising, even when modeling is performed using very simple audio and visual features.
A hybrid technique for face detection in color images

In this paper, a hybrid technique for face detection in color images is presented. The proposed technique combines three analysis models, namely skin detection, automatic eye localization, and appearance-based face/nonface classification. Using a robust histogram-based skin detection model, skin-like pixels are first identified in the RGB color space. Based on this, face bounding-boxes are extracted from the image. On detecting a face bounding-box, approximate positions of the candidate mouth feature points are identified using the redness property of image pixels. A region-based eye localization step, based on the detected mouth feature points, is then applied to face bounding-boxes to locate possible eye feature points in the image. Based on the distance between the detected eye feature points, face/non-face classification is performed over a normalized search area using the Bayesian discriminating feature (BDF) analysis method. Some subjective evaluation results are presented on images taken using digital cameras and a Webcam, representing both indoor and outdoor scenes.
Game, shot and match: Event-based indexing of tennis

Identifying events in sports video offers great potential for advancing visual sports coaching applications. In this paper, we present our results for detecting key events in a tennis match. Our overall goal is to automatically index a complete tennis match into all the main tennis events, so that a match can be recorded using affordable visual sensing equipment and then be automatically indexed into key events for retrieval and editing. The tennis events detected in this paper are a tennis game, a change of end and a tennis serve — all of which share temporal commonalities. There are of course other events in tennis which we aim to index in our overall indexing system, but this paper focuses solely on the aforementioned tennis events. This paper proposes a novel approach to detect key events in an instrumented tennis environment by analysing a players location and the visual features of a player.