nils-herrmann/clean_scirepeval huggingface dataset:
Deep dictionary learning for fine-grained image classification

Fine-grained image classification is quite challenging due to high inter-class similarity and large intra-class variations. Another issue is the small amount of training images with a large number of classes to be identified. To address the challenges, we propose a model for fine-grained image classification with its application to bird species recognition. Based on the features extracted by bilinear convolutional neural network (BCNN), we propose an on-line dictionary learning algorithm where the principle of sparsity is integrated into classification. The features extracted by BCNN encode pairwise neuron interaction in a translation-invariant manner. This property is valuable to fine-grained classification. The proposed algorithm for dictionary learning further carries out sparsity based classification, where training data can be represented with a less number of dictionary atoms. It alleviates the problems caused by insufficient training data, and makes classification much more efficient. Our approach is evaluated and compared with the state-of-the-art approaches on the CUB-200-2011 dataset. The promising experimental results demonstrate its efficacy and superiority.
Real-Time Modeling of 3-D Soccer Ball Trajectories From Multiple Fixed Cameras

In this paper, model-based approaches for real-time 3-D soccer ball tracking are proposed, using image sequences from multiple fixed cameras as input. The main challenges include filtering false alarms, tracking through missing observations, and estimating 3-D positions from single or multiple cameras. The key innovations are: 1. incorporating motion cues and temporal hysteresis thresholding in ball detection; 2. modeling each ball trajectory as curve segments in successive virtual vertical planes so that the 3-D position of the ball can be determined from a single camera view; and 3. introducing four motion phases (rolling, flying, in possession, and out of play) and employing phase-specific models to estimate ball trajectories which enables high-level semantics applied in low-level tracking. In addition, unreliable or missing ball observations are recovered using spatio-temporal constraints and temporal filtering. The system accuracy and robustness are evaluated by comparing the estimated ball positions and phases with manual ground-truth data of real soccer sequences.
Architecture and algorithms for tracking football players with multiple cameras

A system architecture and method for tracking people is presented for a sports application. The system input is video data from static cameras with overlapping fields-of-view at a football stadium. The output is the real-world, real-time positions of football players during a match. The system comprises two processing stages, operating on data from first a single camera and then multiple cameras. The organisation of processing is designed to achieve sufficient synchronisation between cameras, using a request-response pattern, invoked by the second stage multi-camera tracker. The single-view processing includes change detection against an adaptive background and image-plane tracking to improve the reliability of measurements of occluded players. The multiview process uses Kalman trackers to model the player position and velocity, to which the multiple measurements input from the single-view stage are associated. Results are demonstrated on real data.
Multi-camera colour tracking

We propose a colour tracker for use in visual surveillance. The tracker is part of a framework designed to monitor a dynamic scene with more than one camera. Colour tracking complements spatial tracking: it can also be used over large temporal intervals, and between spatially uncalibrated cameras. The colour distributions from objects are modelled, and measures of difference between them are discussed. A context is required for assessing the significance of any difference. It is provided by an analysis of the noise processes: first on the camera capture, then on the underlying variability of the signal. We present results comparing parametric and explicit representations, the inclusion and omission of intensity data, and single and multiple cameras.
A multi-agent framework for visual surveillance

We describe an architecture for implementing scene understanding algorithms in the visual surveillance domain. To achieve a high level description of events observed by multiple cameras, many inter-related event-driven processes must be executed. We use the agent paradigm to provide a framework in which these processes can be managed. Each camera has an associated agent, which detects and tracks moving regions of interest. This is used to construct and update object agents. Each camera is calibrated so that image co-ordinates can be transformed into ground plane locations. By comparing properties, two object agents can infer that they have the same referent, i.e. that two cameras are observing the same entity, and as a consequence merge identities. Each object's trajectory is classified with a type of activity, with reference to a ground plane agent. We demonstrate objects simultaneously tracked by two cameras, which infer this shared observation. The combination of the agent framework, and visual surveillance application provides an excellent environment for development and evaluation of scene understanding algorithms.
Maximizing information about a noisy signal with a single non-linear neuron

For noise-free information maximization, the output signal entropy must be maximized. This is not true for a noisy input: rather, it must be the difference between this entropy and the residual output uncertainty. A definition of information density is introduced, which provides a discrete local measure of bandwidth efficiency. Novel training rules are proposed which enforce a uniformity of this density. This entails a different transfer function from that which follows from the maximization of output entropy alone. It is shown to provide higher information transmission properties on real and synthetic data.
A general framework for 3D soccer ball estimation and tracking

A general framework for automatic 3D soccer ball estimation and tracking from multiple image sequences is proposed. Firstly, the ball trajectory is modelled as planarcurves in consecutive virtual vertical planes. These planes are then determined by two ball positions with accurately estimated height, namely critical points, which are extracted by curvature thresholding and nearest distance of 3D lines from single or multiple views respectively. Finally, unreliable or missing ball observations are recovered using geometric constraints and polynomial interpolation. Experiments on video sequences from different cameras, with over 5000 frames each, have demonstrated a comprehensive solution for accurate and robust 3D ball estimation and tracking, with over 90% ball estimations within 2.5 metres of manually derived ground truth.
Integral Line Scan Features for Pedestrian Detection

This paper presents a novel approach for pedestrian detection using oriented line scans of gradients computed from a gray level image. Three feature types are proposed that can be generated easily from oriented gradients and an effective use of integral lines and integral images. A scalable cascaded classifier is built by combining oriented gradients with the oriented line scan features in a boosting framework. The detector's performance is comparable to the state of the art results and achieves about 3 to 5 fps on 320 x 240 resolution images making the proposed method suitable for real time applications. Detector performance is also represented as PUR, percentage of uncertainty removed.
Towards plug-and-play visual surveillance: learning tracking models

In typical visual surveillance implementations, observations of scene objects are extracted as regions of moving pixels identified by pixel differencing based motion detection algorithms. These observations are tracked to establish their temporal coherence by updating a state vector describing the projected 2D width and height as well as image trajectory. Such an approach is particularly vulnerable to fragmentation and occlusion process as there is essentially no appearance model. The objective of this work is to develop simple but highly discriminatory models of scene objects which indirectly use the depth of the object to model its projected width and height. Rather than relying on a time-consuming, labour-intensive and expert-dependent calibration procedure to recover the full image to ground-plane homography, the system relies on a simple learning procedure involving watching several hundred objects entering, passing through and leaving the monitored view volume to recover the relationship between the projected 2D width and height of an object and its image position and visual motion.
Vehicle logo recognition using Local Fisher Discriminant Analysis

This paper presents a method for localising and recognising vehicle manufacturer logos in both the front and rear views. The method assumes that the vehicle registration plate is visible and an estimate of its location is available. Features are constructed out of local histograms of gradients, in both conventional and hierarchical arrangements. The dimensionality of these vectors is then reduced using unsupervised PCA and, subsequently a supervised method based on Local Fisher Discriminant Analysis. This then provides a suitable metric for logo detection, localisation and multi-class classification. On a test set of data captured from a medium range CCTV camera, with five different manufacturers' logos, the proposed method provided a correct logo localisation rate of 97 % and a correct logo classification rate of 87.6%.
Evaluating the performance of systems for tracking football players and ball

In this paper, we discuss the different approaches used for evaluating the results of tracking algorithms, and in particular for analysis of football (soccer) tracking results. The focus of the study is on systems with multiple static cameras. The appropriate data representation and ground truth capture methods are discussed, and evaluation measures that indicate the performance of any given automatic tracker are presented. The evaluation method is demonstrated to compare results of an implemented multi-camera tracker.
Curious George: An Integrated Visual Search Platform

This paper describes an integrated robot system, known as Curious George, that has demonstrated state-of-the-art capabilities to recognize objects in the real world. We describe the capabilities of this system, including: the ability to access web-based training data automatically and in near real-time, the ability to model the visual appearance and 3D shape of a wide variety of object categories, navigation abilities such as exploration, mapping and path following, the ability to decompose the environment based on 3D structure, allowing for attention to be focused on regions of interest, the ability to capture high-quality images of objects in the environment, and finally, the ability to correctly label those objects with high accuracy. The competence of the combined system has been validated by entry into an international competition where Curious George has been among the top performing systems each year. We discuss the implications of such successful object recognition for society, and provide several avenues for potential improvement.
An extension of the AVC file format for Video Surveillance

This paper introduces and discusses a recent standardization project by the MPEG group that aims at providing an interoperable file format tailored to the needs of the surveillance industry. The new standard is called Video Surveillance Application Format and extends the H.264 — AVC file format specification. Based on a review of the application requirements, the rationale is provided for the technical design choices, and an example system implementation is described in detail. This provides interoperability at the data format and metadata levels. An example reference implementation instantiates and tests the design. The resultant is the specification of a file format suitable for large-scale camera network deployment.
Design of a hybrid multi-occupant visitor communication and door control system

Door phone systems, allowing occupants of a building to communicate with visitors at the door, have evolved over the years, with the current advancements being a fully internet protocol (IP) based solution. In order to adopt newer IP based solutions, current analogue systems can be replaced, yet this may be costly and cumbersome, especially in a conventional multi-occupant building. We therefore propose an architecture which supports current analogue door phone systems, and also provides IP based functionality. We have implemented the proposed architecture based on SIP, WebRTC and an IoT gateway system connected to the multi-occupant conventional video door phone system.
Evaluation of shadow classification techniques for object detection and tracking

In a football stadium environment with multiple overhead floodlights, many protruding shadows can be observed originating from each of the targets. To track individual targets successfully, it is essential to achieve an accurate representation of the foreground. Many existing techniques are sensitive to shadows, falsely classifying shadows as foreground. The paper presents four different techniques associated with shadow classification. Three of the classifiers originate from the review material whilst the fourth is a novel application of a real-time implementation of the k-nearest neighbour algorithm to shadow identification. To assess the performance for each of the classifiers, four quantitative evaluation metrics are proposed. Using each of the evaluation metrics, we discuss the performance of each classifier's segmentation results and assess their impact on the tracking performance.
Efficient PDM shape fitting using the Kalman filter

While the ability of point distribution models to model complex deformable shapes is highly attractive, recovering shape instances is difficult in images containing multiple occluded and occluding shapes located in background clutter. The standard local refinement approach employed within the literature relies on the availability of good initial estimates. A highly efficient search strategy is presented for generating all plausible initial solutions by embedding a Kalman filter in a breadth-first search algorithm to use candidate observations extracted from the image to update the shape parameters of shape hypotheses and constrain the position of subsequent observations.
Technologies for Granting Balance between Security and Privacy in Video-Surveillance

Protecting citizens' privacy is a critical issue in the nowadays society. As data collection capabilities increase with the introduction of new technologies for automatic data capturing, new threats related to privacy protection need to be faced. In this field, video surveillance represents a critical technology: the existence of large amounts of stored personal data increases the probability of personal data misuse. In this paper, a technology that tackles the complex challenge of balancing security and privacy in video-surveillance is presented. The technology has potential for a greater social acceptance of video surveillance technology, due to the inclusion of the value of privacy alongside the enhanced security.
Automatic Removal of Photographic Paper Texture from Digitised Images

Older photographs were frequently printed onto textured paper. This texture is especially noticeable on scanned digital copies of the photograph. The components of the texture can be identified in the frequency domain. A filter can be designed to remove these components and hence this background texture from the image. A method is proposed to automatically adapt the parameters of the filter to the specific texture that is encountered. The method is demomstrated on reed data.
Modelling profiles with a mixture of Gaussians

Point distribution models are useful tools for modelling the variability of particular classes of shapes. A common approach is to apply a principle component analysis to the data, to reduce the dimensionality of the representation. However, a single multivariate Gaussian model of the probability density, estimated from the principle covariances, can be substantially inaccurate. We examine how the specificity of a model can be improved by using a mixture of Gaussians, trained with the expectation-maximization algorithm, with reference to hand and vehicle profiles.
Evaluation of MPEG7 color descriptors for visual surveillance retrieval

This paper presents the results to evaluate the effectiveness of MPEG 7 color descriptors in visual surveillance retrieval problems. A set of image sequences of pedestrians entering and leaving a room, viewed by two cameras, is used to create a test set. The problem posed is the correct identification of other sequences showing the same person as contained in an example image. Color descriptors from the MPEG7 standard are used, including dominant color, color layout, color structure and scalable color experiments are presented that compare the performance of these, and also compare automatic and manual techniques to examine the sensitivity of the retrieval rate on segmentation accuracy. In addition, results are presented on innovative methods to combine the output from different descriptors, and also different components of the observed people. The evaluation measure used is the ANMRR, a standard in content-based retrieval experiments.
Adaptive eigen-backgrounds for object detection

Most tracking algorithms detect moving objects by comparing incoming images against a reference frame. Crucially, this reference image must adapt continuously to the current lighting conditions if objects are to be accurately differentiated. In this work, a novel appearance model method is presented based on the eigen-background approach. The image can be efficiently represented by a set of appearance models with few significant dimensions. Rather than accumulating the necessarily enormous training set to generate the eigen model, the described technique builds and adapts the eigen-model online evolving both the parameters and number of significant dimension. For each incoming image, a reference frame may be efficiently hypothesized from a subsample of the incoming pixels. A comparative evaluation that measures segmentation accuracy using large amounts of manually derived ground truth is presented.
Re-identification of pedestrians with variable occlusion and scale

This paper presents results from experiments designed to measure the accuracy with which people can be reidentified using multiple visual surveillance observations. Two public data sets are used: VIPeR and a new public data set, V-47. The re-identification method is a Large Margin Nearest Neighbour classifier using feature vectors constructed from overlapping block histograms. The experiments investigate the performance with respect to the level of occlusion, the training regime, specificity of the domain and the resolution of the observations. A method is proposed that reduces the adverse impact of occlusions, when present; and increases the beneficial impact of higher resolution data, when available.
NoobLab: An Intelligent Learning Environment for Teaching Programming

Computer programming is a highly practical subject and it is essential that those new to the discipline engage in hands-on experimentation as part of the learning process. However, when faced with large cohorts and an increasing demand for distance and student flexible learning, incorporating this into a programming course can be difficult. There is a dynamic that exists between tutor and student in a real-world programming workshop session that is not easily replicated online. In this paper we describe an online learning environment that begins to create an analogue of this dynamic and its successful integration into an undergraduate programming module. Ultimately, the potential exists to not only improve the student learning experience but also investigate and inform programming pedagogy itself.
Tracking football players with multiple cameras

A system is described for tracking the positions of football players during a match. The input is eight video streams from static cameras, each processed to generate measurements of the players for a multi view tracker. The single view processing includes foreground detection and a bounding box tracker designed to split measurements of merged players. In the multi-view process, the measurements, each from a different camera and matched to the same player, are fused into an overall measurement which is then used in the estimate updating. Results are demonstrated on real data.
Detecting periodic structure

We present a method for the detection of periodic structure in images. Finding a global threshold to detect a significant frequency component, and then associating it with responsible features, is fragile. The method extracts features from the original signal, and uses their autocorrelation as the basis for an adaptive filter, with which they are convolved. This convolution represents the conformity of a local neighbourhood to the dominant spectral frequencies: combined with the original feature signal, it effectively incorporates the property of periodicity into a local feature strength. A recursive implementation is suggested. The method avoids the fragility associated with global thresholds, and is shown to work on real data.
Camera self-calibration from triplets of images using bivariate polynomials derived from Kruppa's equations

In this paper, new equations for the self-calibration of a moving camera with unchanged intrinsic parameters are proposed. Unlike most existing methods that require solving equations in three or more unknowns, our equations are only bivariate. The two unknowns, in our equations, are the scale factors that are responsible for the nonlinearity of Kruppa's equations due to a triplet of images. Once the scale factors are calculated, Kruppa's coefficients are linearly retrieved. The results of our experiments, conducted on simulated and real data, are also presented.
A Novel Clustering-Based Method for Adaptive Background Segmentation

This paper presents a new histogram-based method for dynamic background modeling using a sequence of images extracted from video. In particular, a k-means clustering technique has been used to identify the foreground objects. Because of its shadow resistance and discriminative properties, we have used images in the HSV color space instead of the traditional RGB color space. The experimental results on real images are very encouraging as we were able to retrieve perfect backgrounds in simple scenes. In very complex scenes, the backgrounds we have obtained were very good. Furthermore, our method is very fast and could be used in real-time applications after optimization.
Self-calibration of a simplified camera using Kruppa equations

In this paper, an original and simplified formulation of the Kruppa's equations is presented. This formulation is particularly of interest when only two images are available. Indeed, given two images taken with a camera with zero skew and known principal point, we show that there are only three possible solutions for the horizontal and vertical scale factors instead of four as widely believed so far. In addition, when the aspect ratio is unity, the horizontal and vertical scale factors are equal and the solution is unique and straightforward. No additional knowledge on the scene is required. The issues of refining the solution and the influence of the principal point are addressed and discussed in our experiments.