nils-herrmann/clean_scirepeval huggingface dataset:
Vehicle detection, tracking and classification in urban traffic

This paper presents a system for vehicle detection, tracking and classification from roadside CCTV. The system counts vehicles and separates them into four categories: car, van, bus and motorcycle (including bicycles). A new background Gaussian Mixture Model (GMM) and shadow removal method have been used to deal with sudden illumination changes and camera vibration. A Kalman filter tracks a vehicle to enable classification by majority voting over several consecutive frames, and a level set method has been used to refine the foreground blob. Extensive experiments with real world data have been undertaken to evaluate system performance. The best performance results from training a SVM (Support Vector Machine) using a combination of a vehicle silhouette and intensity-based pyramid HOG features extracted following background subtraction, classifying foreground blobs with majority voting. The evaluation results from the videos are encouraging: for a detection rate of 96.39%, the false positive rate is only 1.36% and false negative rate 4.97%. Even including challenging weather conditions, classification accuracy is 94.69%.
PRISMATICA: a multi-sensor surveillance system for public transport networks

PRISMATICA (PRoactive Integrated systems for Security Management by Technological, Institutional and Communication Assistance)a system for multiple-sensor surveillance in key public transport networks has been described. The design was closely inspired by what public transport operators are familiar with, i.e. the concepts of distributed sensors, distributed monitoring and decision making in a control room environment. Results have been presented that demonstrate the capability of automatic systems to detect potentially dangerous situations and of a distributed system that can promptly alert operators providing multiple sensor views of the events. Real-world demonstrators on key sites have demonstrated the feasibility of the approach.
Automatic congestion detection system for underground platforms

An automatic monitoring system is proposed in this paper for detecting overcrowding conditions in the platforms of underground train services. Whenever overcrowding is detected, the system will notify the station operators to take appropriate actions to prevent accidents, such as people falling off or being pushed onto the tracks. The system is designed to use existing closed circuit television (CCTV) cameras for acquiring images of the platforms. In order to focus on the passengers on the platform, background subtraction and update techniques are used. In addition, due to the high variation of brightness on the platforms, a variance filter is introduced to optimize the removal of background pixels. A multi-layer feed forward neural network was developed for classifying the levels of congestion. The system was tested with recorded video from the London Bridge station, and the testing results were shown to be accurate in identifying overcrowding conditions for the unique platform environment.
Crowd monitoring using image processing

The understanding of crowd behaviour in semi-confined spaces is an important part of the design of new pedestrian facilities, for major layout modifications to existing areas and for the daily management of sites-subject to crowd traffic. Conventional manual measurement techniques are not suitable for comprehensive data collection of patterns of site occupation and movement. Real-time monitoring is tedious and tiring, but safety-critical. This paper presents some image processing techniques being developed at King's College London which, using existing closed-circuit television systems, can support both data collection and on-line monitoring of crowds. The application of these methods could lead to a better understanding of crowd behaviour, improved design of the built environment and increased pedestrian safety.
Selecting and evaluating data for training a pedestrian detector for crowded conditions

Computer vision algorithms for pedestrian detection are often based on classification derived from supervised learning and therefore require training data, which can be built by using generic or specific images. In this field, INRIA datasets are a standard reference but include only few CCTV camera samples. Therefore, for a CCTV camera system it might be interesting to have specific training data. However, in practice it is impossible to create a training data for each camera view. Thus, this paper presents an evaluation of a pedestrian detection algorithm in crowded conditions in relation to the training data, and shows that a CCTV camera training data provides better results and can be reused for similar CCTV camera views.
Structural Laplacian Eigenmaps for Modeling Sets of Multivariate Sequences

A novel embedding-based dimensionality reduction approach, called structural Laplacian Eigenmaps, is proposed to learn models representing any concept that can be defined by a set of multivariate sequences. This approach relies on the expression of the intrinsic structure of the multivariate sequences in the form of structural constraints, which are imposed on dimensionality reduction process to generate a compact and data-driven manifold in a low dimensional space. This manifold is a mathematical representation of the intrinsic nature of the concept of interest regardless of the stylistic variability found in its instances. In addition, this approach is extended to model jointly several related concepts within a unified representation creating a continuous space between concept manifolds. Since a generated manifold encodes the unique characteristic of the concept of interest, it can be employed for classification of unknown instances of concepts. Exhaustive experimental evaluation on different datasets confirms the superiority of the proposed methodology to other state-of-the-art dimensionality reduction methods. Finally, the practical value of this novel dimensionality reduction method is demonstrated in three challenging computer vision applications, i.e., view-dependent and view-independent action recognition as well as human-human interaction classification.
Toward a Two-Handed Gesture-Based Visual 3D Interactive Object-Oriented Environment for Software Development

This paper presents the conceptual and architectural design of a multilayered framework aiming to provide a two handed gesture based visual interactive 3D object oriented environment for software development. We argue that this is a viable, intuitive and attractive approach for software development facilitating natural human computer interaction, thus supporting tasks related to software development in a more effective and efficient way. We illustrate the value of the proposed environment through a data analysis from early evaluations of one prototype system in use. The results and implications of this study are useful for designing effective and efficient gesture based 3D interactive software development environments.
Urban road user detection and classification using 3D wire frame models

This study presents a detection and classification system for vehicles and pedestrians in urban traffic scenes. This aims to guide surveillance operators and reduce human resources for observing hundreds of cameras in urban traffic surveillance. The authors perform per frame vehicle detection and classification using 3D models on calibrated cameras. Motion silhouettes (from background estimation) are extracted and compared to a projected model silhouette to identify the ground plane position and class of vehicles and pedestrians. The system is evaluated with the reference i-LIDS datasets from the UK Home Office. Performance for varying numbers of classes for three different weather conditions and for different video input filters is evaluated. The full system including detection and classification achieves a recall of 87% at a precision of 85.5% outperforming similar systems in the literature. The i-LIDS dataset is available to other researchers to compare with our results. The authors conclude with an outlook to use local features for improving the classification and detection performance.
Intelligent distributed surveillance systems: a review

This survey describes the current state-of-the-art in the development of automated visual surveillance systems so as to provide researchers in the field with a summary of progress achieved to date and to identify areas where further research is needed. The ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors is essential for automated visual surveillance. The increasing need for intelligent visual surveillance in commercial, law enforcement and military applications makes automated visual surveillance systems one of the main current application domains in computer vision. The emphasis of this review is on discussion of the creation of intelligent distributed automated surveillance systems. The survey concludes with a discussion of possible future directions.
Motorcycle Classification in Urban Scenarios using Convolutional Neural Networks for Feature Extraction

This paper presents a motorcycle classification system for urban scenarios using Convolutional Neural Network (CNN). Significant results on image classification has been achieved using CNNs at the expense of a high computational cost for training with thousands or even millions of examples. Nevertheless, features can be extracted from CNNs already trained. In this work AlexNet, included in the framework CaffeNet, is used to extract features from frames taken on a real urban scenario. The extracted features from the CNN are used to train a support vector machine (SVM) classifier to discriminate motorcycles from other road users. The obtained results show a mean accuracy of 99.40% and 99.29% on a classification task of three and five classes respectively. Further experiments are performed on a validation set of images showing a satisfactory classification.
Automatic Segmentation and Recognition of Human Actions in Monocular Sequences

This paper addresses the problem of silhouette-based human action segmentation and recognition in monocular sequences. Motion History Images (MHIs), used as 2D templates, capture motion information by encoding where and when motion occurred in the images. Inspired by codebook approaches for object and scene categorization, we first construct a codebook of temporal motion templates by clustering all the MHIs of each particular action. These MHIs capture different actors, speeds and a wide range of camera viewpoints. In this paper, we use a Kohonen's Self-Organizing Map (SOM) to simultaneously cluster the MHI templates and represent them in lower dimensional subspaces. To cope with temporal segmentation, and concurrently carry out action recognition, a new architecture is proposed where the obsrvation MHIs are projected onto all these action-specific manifolds and the Euclidean distance between each MHI and the nearest cluster within each action-manifold constitutes the observation vector of a Markov Model. To estimate the state/action at each time step, we introduce a new method based on Observable Markov Models (OMMs) where the Markov model is augmented with a neutral state. The combination of our action-specific manifolds with the augmented OMM allows to automatically segment and recognize long sequences of consecutive actions, without any prior knowledge about initial and ending frames of each action. Importantly, our method allows to interpolate betweeen training viewpoint and recognizes actions, independently of the camera viewpoint, even from unseen viewpoints.
An intelligent distributed surveillance system for public transport

We outline the main features of a distributed surveillance system developed for public transport applications, primarily for metropolitan railway networks, as part of the PRISMATICA project. The system has been developed using detailed studies of the needs of end-users, including managers, control room operators and passengers. This has been done by investigating not only actual events that need to be detected, but also situations that lead to reduced levels of perceived security (such as the feeling of isolation in particular environments). Typical control room operations have been studied in detail, and the system design has been inspired by, and matches, the distributed nature of the surveillance tasks, in terms of both geographical distribution and the variety of information processes. Variability and scalability has been dealt with by designing and implementing a generic XML-messaging system (so that it is the devices themselves that determine how they are controlled and what sort of information they can provide) operating on a CORBA-based architecture. This has allowed the integration of a range of detection systems including video processing, audio processing and radio-based detection of mobile panic buttons (as part of a smart card). The system has been tried successfully in two major environments: the London and Paris underground railway systems. (5 pages)
A supervised method for retinal blood vessel segmentation using line strength, multiscale Gabor and morphological features

The change in morphology, diameter, branching pattern and/or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports a supervised methodology for segmentation of the retinal vasculature from ocular fundus images. A 7-D feature vector is constructed by computing the outputs of morphological linear operators, line strengths and oriented Gabor filters at multiple scales. The feature vector encodes the spatial intensity measures along with vessel geometry at multiple scales. A Bayesian Classifier; the Gaussian Mixture Model is used for classification of the retinal image into vessels and non-vessel pixels. The methodology is evaluated using the images of two publicly available databases, the DRIVE database and the STARE database. Method performance on both sets of test images is better than the 2nd human observer and other existing methodologies available in the literature.
ViHASi: Virtual human action silhouette data for the performance evaluation of silhouette-based action recognition methods

In this paper we introduce a large body of virtual human action silhouette (ViHASi) data that we have recently generated for the purpose of evaluating a family of action recognition methods. These are the silhouette-based human action recognition methods. This synthetic multi-camera video data-set consists of 20 action classes, 9 actors and up to 40 synchronized perspective cameras. This data-set has been recently made available online for other researchers to download. In order to demonstrate the usefulness of the ViHASi data we make use of an existing action recognition method that is simple and relatively fast. Moreover, to deal with long video sequences containing several action samples, a practical temporal segmentation algorithm is introduced and tested that is tightly coupled with the action recognition method used. The experimental methodologies outlined here provides a route towards quantitatively comparing silhouette-based action recognition methods.
Multi-view human action recognition using 2D motion templates based on MHIs and their HOG description

In this study, a new multi-view human action recognition approach is proposed by exploiting low-dimensional motion information of actions. Before feature extraction, pre-processing steps are performed to remove noise from silhouettes, incurred due to imperfect, but realistic segmentation. Two-dimensional motion templates based on motion history image (MHI) are computed for each view/action video. Histograms of oriented gradients (HOGs) are used as an efficient description of the MHIs which are classified using nearest neighbor (NN) classifier. As compared with existing approaches, the proposed method has three advantages: (i) does not require a fixed number of cameras setup during training and testing stages hence missing camera-views can be tolerated, (ii) requires less memory and bandwidth requirements and hence (iii) is computationally efficient which makes it suitable for real-time action recognition. As far as the authors know, this is the first report of results on the MuHAVi-uncut dataset having a large number of action categories and a large set of camera-views with noisy silhouettes which can be used by future workers as a baseline to improve on. Experimentation results on multi-view with this dataset gives a high-accuracy rate of 95.4% using leave-one-sequence-out cross-validation technique and compares well to similar state-of-the-art approaches.
Analytical least squares Hough transform with an implementation on a transputer network

Computer vision for real-world imagery normally consists of three stages: acquisition/low-level feature extraction (e.g. capture followed by edge detection), medium-level feature extraction (typically into a geometric and/or topological space) and task-oriented scene understanding (e.g. aggregation of geometric features to characterise objects of interest). The Hough transform (HT) is an efficient medium level 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. This paper describes two HT variants based on an analytical least squares refinement procedure that helps overcome some of these difficulties. A parallel implementation on a transputer based system is also discussed and evaluated.<<ETX>>
Recognizing Human Actions Using Silhouette-based HMM

This paper addresses the problem of silhouette-based human action modeling and recognition, specially when the number of action samples is scarce. The first step of the proposed system is the 2D modeling of human actions based on motion templates, by means of Motion History Images (MHI). These templates are projected into a new subspace using the Kohonen Self Organizing feature Map (SOM), which groups viewpoint (spatial) and movement (temporal) in a principal manifold, and models the high dimensional space of static templates.The next step is based on the Hidden Markov Models (HMM) in order to track the map behavior on the temporal sequences of MHI. Every new MHI pattern is compared with the features map obtained during the training. The index of the winner neuron is considered as discrete observation for the HMM. If the number of samples is not enough, a sampling technique, the Sampling Importance Resampling (SIR) algorithm, is applied in order to increase the number of observations for the HMM. Finally, temporal pattern recognition is accomplished by a Maximum Likelihood (ML) classifier. We demonstrate this approach on two publicly available dataset: one based on real actors and another one based on virtual actors.
Pedestrian detection based on adaboost algorithm with a pseudo-calibrated camera

This paper presents a new algorithm for pedestrian detection for a fixed camera using the cluster boosted tree (CBT) structure of Wu and Nevatia for building a multi-view tree classifier based on edgelet features. The main advantage of this structure is that it is less sensitive to camera view changes compared to the cascade structure of Viola and Jones. The approach presented in this paper uses geometrical information in the image to estimate pedestrian size for a given pixel position. This we call pseudo camera calibration. Thereby, we combine the CBT classifier trained on the INRIA datasets and the pedestrian size estimator to detect pedestrians. The performance of this algorithm is also evaluated on images captured at a real metro station for several camera positions.
Oriented texture classification based on self-organizing neural network and Hough transform

This paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's (1990) neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison with an implemented technique based on Gabor filters.
Modelling periodic scene elements for visual surveillance

Some urban scenes exhibit periodic variations that can be relevant to visual surveillance applications. One example is the variation in the background elements, such as those caused by moving escalators, lights and scrolling advertisements. When modelled correctly, the incorporation of these periodic elements as a Markov model in a foreground detection component can improve the performance significantly. Another area where the periodicity in the scene can be used is anomaly detection. In some underground metro stations where the flow of people is periodic, deviations from this periodicity can be interpreted as abnormal movements of people. This can be achieved by using a higher-dimensional model for the underlying data structure, and mapping it to a one-dimensional signal for interpretation. This approach is tested, and the results show that abnormal behaviour can be automatically detected.
Vehicle type categorization: A comparison of classification schemes

This paper describes research to classify road vehicles into a range of broad categories using simple measures of size and shape derived from view-dependent binary silhouettes using images derived from a static roadside CCTV camera. A novel approach to camera calibration utilizes calibrated images mapped by Google Earth to provide accurately-surveyed scene geometry that is manually corresponded with visible groundplane landmarks in the CCTV images. In the experiments reported here, manual segmentation is used to delineate vehicles in the images and a set of scaled features is extracted from each binary silhouette. Classification assigns each blob to one of four vehicle classes (car, van, bus and bicycle/motorcycle) using two feature-based classifiers (SVM and random forests) and a model-based approach. Results are presented for 10-fold cross validation study involving over 2000 manually labeled silhouettes. A peak classification performance of 96.26% is observed for SVM.
From tracking to advanced surveillance

Using a simple but robust real-time algorithm that allows tracking of multiple objects in complex environments, the authors propose a wide range application in automatic surveillance by linking blobs' information, in terms of trajectories and positions, with the events to be detected. Real-time is achieved reducing the computation time avoiding the use of colour in the segmentation stage and statistical prediction in the tracking stage and using instead segmentation based on luminance contrast and tracking based on the overlapping information from bounding boxes from two consecutive frames.
Human action recognition using robust power spectrum features

We propose a new method for human action recognition from video streams that is fast and robust to noise and to large changes in camera views. We start by extracting features in the Fourier domain once we obtain the bounding boxes containing the silhouettes of a human for a number of video frames representing a basic action. After preprocessing, we divide each space-time volume into space-time sub-volumes (STSV) and compute their corresponding mean-power spectra as our feature vectors. Our features result in high classification performance even with simple distance measures. We perform an experimental comparison, using the same data, between our method and two state-of-the-art methods.