nils-herrmann/clean_scirepeval huggingface dataset:
Estimating crowd density with Minkowski fractal dimension

The estimation of the number of people in an area under surveillance is very important for the problem of crowd monitoring. When an area reaches an occupation level greater than the projected one, people's safety can be in danger. This paper describes a new technique for crowd density estimation based on Minkowski fractal dimension. The fractal dimension has been widely used to characterize data texture in a large number of physical and biological sciences. The results of our experiments show that fractal dimension can also be used to characterize levels of people congestion in images of crowds. The proposed technique is compared with a statistical and a spectral technique, in a test study of nearly 300 images of a specific area of the Liverpool Street Railway Station, London, UK. Results obtained in this test study are presented.
Calibration and object correspondence in camera networks with widely separated overlapping views

This study contributes in two ways to the research of multi-camera object tracking in the context of visual surveillance. Firstly, a semi-automatic scene calibration method is proposed to deal with mapping a network of cameras with overlapped fields of view onto a single ground plane view, even when the overlap is not substantial. The proposed method uses a semi-supervised approach that combines tracked blobs with user-selected line scene features to recover the homographies between camera views that are both simple and accurate. Then, based on the scene calibration information, the intersection points of the projected vertical axis of single camera blobs are used to make object correspondences across multiple views. The method works in mixed environments of both pedestrians and vehicles, and is shown to be accurate and robust against segmentation noise and occlusions. Finally, the advantage of the proposed method is demonstrated by quantitative tracking performance evaluation and comparison against previous methods.
Estimation of crowd density using image processing

Human beings perceive images through their properties, like colour, shape, size, and texture. Texture is a fertile source of information about the physical environment. Images of low density crowds tend to present coarse textures, while images of dense crowds tend to present fine textures. The paper describes a technique for automatic estimation of crowd density, which is a part of the problem of automatic crowd monitoring, using texture information based on grey level transition probabilities on digitised images. Crowd density feature vectors are extracted from such images and used by a self organising neural network which is responsible for the crowd density estimation. Results obtained respectively to the estimation of the number of people in a specific area of Liverpool Street Railway Station in London (UK) are presented.
On the efficacy of texture analysis for crowd monitoring

The goal of this work is to assess the efficacy of texture measures for estimating levels of crowd densities in images. This estimation is crucial for the problem of crowd monitoring and control. The assessment is carried out on a set of nearly 300 real images captured from Liverpool Street Train Station, London, UK, using texture measures extracted from the images through the following four different methods: gray level dependence matrices, straight line segments, Fourier analysis, and fractal dimensions. The estimations of crowd densities are given in terms of the classification of the input images in five classes of densities (very low, low, moderate, high and very high). Three types of classifiers are used: neural (implemented according to the Kohonen model), Bayesian, and an approach based on fitting functions. The results obtained by these three classifiers, using the four texture measures, allowed the conclusion that, for the problem of crowd density estimation, texture analysis is very effective.
Portable multi touch TableTop: A composite approach for industrial applications

Digital tabletops are gaining increasing interest within commercial sectors both for home and office applications. These devices, with their multi-touch surfaces, can support collaborative activities while maintaining natural face-to-face communication. However, still one of the major problems is related to their portability and the space they require. We have conducted a study to explore how to design a novel architecture for surfaces that could operate in any environment without space limitations based on multiple tabletops. This paper presents an architecture based on the relative position of the tablets is proposed including an efficient data transmission system in order to obtain real time synchronisation and positioning of multiple tablets using a star topology. (4 pages)
Real-time motion detection of crowds in video signals

A real-time motion detection system consisting of two PCI bus plug and play cards has been developed. The PCI bus is employed as a video bus to transfer digitised video information to the motion estimation board. The motion vectors are continuously delivered to the host via the PCI bus in a DMA transfer mode. The process was developed under the Windows 95 operating system and it was tested with a number of test video sequences. This system is to be implemented as part of a crowd monitoring system.
Learning and Recognizing Human Action from Skeleton Movement with Deep Residual Neural Networks

Automatic human action recognition is indispensable for almost artificial intelligent systems such as video surveillance, human-computer interfaces, video retrieval, etc. Despite a lot of progress, recognizing actions in an unknown video is still a challenging task in computer vision. Recently, deep learning algorithms have proved its great potential in many vision-related recognition tasks. In this paper, we propose the use of Deep Residual Neural Networks (ResNets) to learn and recognize human action from skeleton data provided by Kinect sensor. Firstly, the body joint coordinates are transformed into 3D-arrays and saved in RGB images space. Five different deep learning models based on ResNet have been designed to extract image features and classify them into classes. Experiments are conducted on two public video datasets for human action recognition containing various challenges. The results show that our method achieves the state-of-the-art performance comparing with existing approaches.
A profile of MPEG-7 for visual surveillance

This paper builds on previous work to propose a meta-data standard for video surveillance. The motivation is to promote interoperability. The starting point is the set of requirements under consideration for a Multimedia Application Format. These requirements cover a description of the surveillance system and of the activity in the scene. In addition to this set, appropriate descriptions for the relation between camera and scene are also considered. To improve interoperability between systems and between components of a system, two types of restrictions are proposed. The first proposal is a restricted subset of the MPEG-7 elements that are applicable to the surveillance domain. The second proposal is to use the MPEG-7 tools to include domain-specific taxonomies to restrict the names of elements used in the semantic descriptions. Both proposals are incorporated into examples which demonstrate the use of the standard.
Object classification for real-time video-surveillance applications

Object classification is a fundamental step in automatic video-surveillance that allows improved tracking and a more accurate description of events. However, as real-world applications need a real-time, flexible, easy and quick to configure solution, the design of a practical object classification algorithm becomes a challenge. This paper analyses advantages and disadvantages of different frameworks presented in the literature, with particular focus on the ones that are more suitable for real-world operation. A learning-based solution using a reduced training set is proposed, demonstrating that it overcomes many of the limitations associated with other algorithms.
A Parallel Solution Of Inverse Kinematics For The Rtx Robot Using Ada And Transputers

In adaptive robots on-line sensor corrections are generally specified relative to a "world" co-ordinate system, whereas the manipulator is actuated in a joint co-ordinate system. Thus the real-time execution of what is known as the Inverse Kinematics Algorithm (IKA) is very important. To decrease execution times, parallel processing supported by a multi-processor architecture were used. Of special interest was the use of the Ada programming language, normally associated with large systems, and its inherent facilities for parallel processing. Taking as an example an inverse kinematics algorithm for the RTX robot, a method was used to change the algorithm from a sequential form to a parallel form suitable for implementation on a multi-processor system. The Algorithm was then implemented on Ada targeted to a multi-transputer system. Performance was then evaluated for a variety of transputer configurations and compared to equivalent implementations written in Occam. We conclude that although there are a number of transputer configurations that can fulfil realistic performance criteria, the selected implementation of Ada does not make it a viable option for a cost-effective implementation of co-ordinate transformation for the selected robot.
A DSP-based system for the detection of vehicles parked in prohibited areas

In this paper, a system for automatic robust video surveillance is described and in particular its application to the problem of locating vehicles that stop in prohibited area is discussed. The structure of software for video processing (alarm generation, interface with the operator and information storage) is outlined together with the hardware (Trimedia DSP boards and industrial computers) which constitutes an industrial-grade product. The emphasis on this paper is to demonstrate robust detection and hence we show the results of a performance evaluation process carried out with the UK's i-LIDS "Parked Vehicle " reference dataset.
3D Interaction Environment for Free View Point TV and Games Using Multiple Tablet Computers

The use of 3D technologies to represent elements and interact with them is an open and interesting research area. In this article we discuss a novel human computer interaction method that integrates mobile computing and 3D visualization techniques with applications on free viewpoint visualization and 3D rendering for interactive and realistic environments. Especially this approach is focused on augmented reality and home entertainment and it was developed and tested on mobiles and particularly on tablet computers. Finally, an evaluation mechanism on the accuracy of this interaction system is presented.
A gripper and sensor system for controlled slip and force

The paper describes the development of a robot gripper, sensors and associated electronics suitable for controlling slippage and contact force. A typical application is the handling of glass test tubes in an automated medical analysis laboratory. Through force control, the gripper first holds an object, then repeatedly releases and regrasps it for controlled slip. The basic mechanical principle consists of a rolling cylinder in direct contact with the (cylindrical) object, forming a cylinder-cylinder pivot with 5 degrees of freedom. A stable grasp is achieved by a finger/counter-finger arrangement with 3 contact points. In the left finger, contact force is reflected in the pivots of the roll and measured by piezoresistive force sensors. In the right finger, motion is measured by optical encoders. Analog electronics deal with gripper motor control and signal processing for the force sensors. Digital circuits handle the motion measurement from the optical encoders, A/D conversion of force sensor outputs and force feedback for motor control and communications with an external computer. The proposed design has been built and tested using an industrial SCARA robot.<<ETX>>
Line and circle finding by the weighted Mahalanobis distance transform and extended Kalman filtering

The paper presents a new parameter space approach, called the Weighted Mahalanobis Distance Hough Transform (WMDHT) whose main merit is to incorporate formal stochastic image and feature noise models. It is aimed at improving the efficiency, accuracy and reducing the size of the accumulator arrays by combining it with extended Kalman filter refinement. It works by detecting image feature points in the neighbourhood of a contour instead of exactly on the contour through a Mahalanobis distance measure modified by a weight function inversely proportional to the distance between the point and an ideal contour. The method is applicable to geometric features of any dimensionality and the paper illustrates it by considering detection of straight and circular segments.<<ETX>>
F1 score assesment of gaussian mixture background subtraction algorithms using the MuHAVi dataset

Background subtraction algorithms are mainly used to segment some specific moving objects in an image sequences. Within of the action recognition context, these methods may be proper to generate automatically silhouettes of the human actions. In this way, MuHAVi is a human action dataset which provides a small set of manually annotated silhouettes and a large set of multicamera raw video. The purpose of this work is to use a segmentation algorithm to generate automatically the whole dataset of silhouettes for the MuHAVi raw video. The F1-score unit measurement is the selection criterion as for the best method to generate such silhouettes. This paper focuses especially on background subtraction methods that create a statistical model of the background, typically using a mixture of Gaussian. The best-evaluated algorithm can then be used to generate automatically a set of silhouettes.
Local Fisher Discriminant Analysis for Pedestrian Re-identification

Metric learning methods, for person re-identification, estimate a scaling for distances in a vector space that is optimized for picking out observations of the same individual. This paper presents a novel approach to the pedestrian re-identification problem that uses metric learning to improve the state-of-the-art performance on standard public datasets. Very high dimensional features are extracted from the source color image. A first processing stage performs unsupervised PCA dimensionality reduction, constrained to maintain the redundancy in color-space representation. A second stage further reduces the dimensionality, using a Local Fisher Discriminant Analysis defined by a training set. A regularization step is introduced to avoid singular matrices during this stage. The experiments conducted on three publicly available datasets confirm that the proposed method outperforms the state-of-the-art performance, including all other known metric learning methods. Further-more, the method is an effective way to process observations comprising multiple shots, and is non-iterative: the computation times are relatively modest. Finally, a novel statistic is derived to characterize the Match Characteristic: the normalized entropy reduction can be used to define the 'Proportion of Uncertainty Removed' (PUR). This measure is invariant to test set size and provides an intuitive indication of performance.
Performance evaluation of event detection solutions: the CREDS experience

In video surveillance projects, automatic and real-time event detection solutions are required to guarantee an efficient and cost-effective use of the infrastructure. Many solutions have been proposed to automatically detect a variety of events of interest. However, not all solutions and technologies may satisfy all the requirements of the surveillance scenario. For this reason, performance evaluation of existing event detection solutions becomes an important step in the deployment of video surveillance projects. In this paper, we propose a practical approach that aims at minimizing the ground truth generation problem and the expertise required to evaluate and compare the results by introducing specific requirements of specific event detection scenarios. This approach is believed to be applicable for an initial evaluation of candidate solutions to a specific surveillance scenario before more exhaustive tests in an integrated environment. The proposed method is under evaluation in the framework of the challenge of real-time event detection solutions (CREDS).
PMHI: Proposals From Motion History Images for Temporal Segmentation of Long Uncut Videos

This letter proposes a method for the generation of temporal action proposals for the segmentation of long uncut video sequences. The presence of consecutive multiple actions in video sequences makes the temporal segmentation a challenging problem due to the unconstrained nature of actions in space and time. To address this issue, we exploit the nonaction segments present between the actual human actions in uncut videos. From the long uncut video, we compute the energy of consecutive nonoverlapping motion history images (MHIs), which provides spatiotemporal information of motion. Our proposals from MHIs (PMHI) are based on clustering the MHIs into actions and nonaction segments by detecting minima from the energy of MHIs. PMHI efficiently segments the long uncut videos into a small number of nonoverlapping temporal action proposals. The strength of PMHI is that it is unsupervised, which alleviates the requirement for any training data. Our temporal action proposal method outperforms the existing proposal methods on the Multi-view Human Action video (MuHAVi)-uncut and Computer Vision and Pattern recognition (CVPR) 2012 Change Detection datasets with an average recall rate of 86.1% and 86.0%, respectively.
Detection and classification of vehicles for urban traffic scenes

This paper presents a vehicle detection and classification system for urban traffic scenes. This aims to guide surveillance operators and reduce human resources for observing hundreds of cameras in urban traffic surveillance. We perform per frame vehicle detection and classification using 3D models. Motion silhouettes are extracted and compared to a projected model silhouette to identify the ground plane position and class of a vehicle. The system is evaluated with the reference i-LIDS dataset from the UK Home Office. Three weather conditions are tested where sunny conditions give the best classification result of 100% outperforming overcast conditions. The full system including detection and classification for all data achieves a recall of 90.4% at a precision of 87.9% outperforming similar systems in the literature. The i-LIDS dataset is available to other researchers to compare with our results. We conclude with an outlook to use local features for improving the classification and detection performance.
Advanced Surveillance: From Tracking To Event Detection

The authors suggest an automatic surveillance system based on a robust and simple algorithm for foreground detection and tracking of multiple objects in complex environments. It is possible to obtain enough information from the characteristics of the detected objects, their centroid's position and their trajectories as to detect some predefined events. Working without the operator supervision the application analyses the video streams from the CCTV surveillance system, discarding empty scenes for monitoring or recording, showing to the operator those fulfilling predefined requirements, like crowding, and rising an alarm to call the operator attention when it decides that an event may be taking place. The application is based on very simple algorithms in order to allow a single computer the analysis of up to eight video streams in real-time. Blob and scene characteristics are used to create a low-level description of predefined events. The application detects events by comparing the sequence's parameters with those associated with the events for the current scenario. The provided examples are tailored to the specific demands of a surveillance system in a public transport environment.
An analytical least squares Hough transform

The Hough Transform (HT) is an efficient method to extract geometric features from an image which works fairly well for images that contain noise and occlusion. However, its performance decreases with image and parameter space quantisation noise. Accuracy is limited by memory costs and oversampling effects. This paper describes two HT variants based on analytical least squares refinement based on a conventional HT voting stage. Both methods reduce memory requirements. A number of experimental results are presented and compared with the standard HT.<<ETX>>
A Review of Computer Vision Techniques for the Analysis of Urban Traffic

Automatic video analysis from urban surveillance cameras is a fast-emerging field based on computer vision techniques. We present here a comprehensive review of the state-of-the-art computer vision for traffic video with a critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). The decreasing hardware cost and, therefore, the increasing deployment of cameras have opened a wide application field for video analytics. Several monitoring objectives such as congestion, traffic rule violation, and vehicle interaction can be targeted using cameras that were typically originally installed for human operators. Systems for the detection and classification of vehicles on highways have successfully been using classical visual surveillance techniques such as background estimation and motion tracking for some time. The urban domain is more challenging with respect to traffic density, lower camera angles that lead to a high degree of occlusion, and the variety of road users. Methods from object categorization and 3-D modeling have inspired more advanced techniques to tackle these challenges. There is no commonly used data set or benchmark challenge, which makes the direct comparison of the proposed algorithms difficult. In addition, evaluation under challenging weather conditions (e.g., rain, fog, and darkness) would be desirable but is rarely performed. Future work should be directed toward robust combined detectors and classifiers for all road users, with a focus on realistic conditions during evaluation.
Performance evaluation of re-acquisition methods for public transport surveillance

Video-based methods such as tracking and appearance recognition can be used in the public transport domain to re-acquire an estimate of the whereabouts of a passenger of interest. Starting with the end user requirements, it is argued that the most appropriate performance metric with which to evaluate these methods, is to measure the information gained through their use. This is equivalent to the reduction in uncertainty about the passenger's whereabouts, after tracking and appearance-based measurements are used to update the estimates of which detected persons are likely to be the passenger of interest. The prior estimates are significantly conditioned on the statistics for the likely distributions for entry and traversal through the network: a simple model is described for this purpose. The suitability of tracking and appearance based matching methods are discussed in this context.