nils-herrmann/clean_scirepeval huggingface dataset:
A method for obtaining neural network training sets in video sequences

A method for video sequence classification is presented as a technique for enhancing the performance of neural network classification systems beyond that obtained by manually selecting the training data. The method uses a stochastic representation of each image frame in a sequence set, to form the basis for texture-based classification of each image. Each frame is allocated into statistically similar bins. The bins are used for discriminating large amounts of data and enhance the possibility of selecting representative vectors when neural network training sets are formed. The technique is shown to be a powerful method for operator independent classification of large sets of video data. The resulting classification is not limited to training sets selection. It can also be used for data mining, video indexing, "ground truth" for algorithm evaluation and classification as well as data profiling.
Communication Mechanisms and Middleware for Distributed Video Surveillance

A new generation of advanced surveillance systems is being conceived as a collection of multisensor components such as video, audio, and mobile robots interacting in a cooperating manner to enhance situation awareness capabilities to assist surveillance personnel. The prominent issues that these systems face are the improvement of existing intelligent video surveillance systems, the inclusion of wireless networks, the use of low power sensors, the design architecture, the communication between different components, the fusion of data emerging from different type of sensors, the location of personnel (providers and consumers), and the scalability of the system. This paper focuses on the aspects pertaining to real-time distributed architecture and scalability. For example, to meet real-time requirements, these systems need to process data streams in concurrent environments, designed by taking into account scheduling and synchronization. This paper proposes a framework for the design of visual surveillance systems based on components derived from the principles of real-time networks/data-oriented requirements implementation scheme. It also proposes the implementation of these components using the well-known middleware technology common object request broker architecture. Results using this architecture for video surveillance are presented through an implemented prototype.
An Optimized and Fast Scheme for Real-Time Human Detection Using Raspberry Pi

Real-time human detection is a challenging task due to appearance variance, occlusion and rapidly changing content; therefore it requires efficient hardware and optimized software. This paper presents a real-time human detection scheme on a Raspberry Pi. An efficient algorithm for human detection is proposed by processing regions of interest (ROI) based upon foreground estimation. Different number of scales have been considered for computing Histogram of Oriented Gradients (HOG) features for the selected ROI. Support vector machine (SVM) is employed for classification of HOG feature vectors into detected and non-detected human regions. Detected human regions are further filtered by analyzing the area of overlapping regions. Considering the limited capabilities of Raspberry Pi, the proposed scheme is evaluated using six different testing schemes on Town Centre and CAVIAR datasets. Out of these six testing schemes, Single Window with two Scales (SW2S) processes 3 frames per second with acceptable less accuracy than the original HOG. The proposed algorithm is about 8 times faster than the original multi-scale HOG and recommended to be used for real-time human detection on a Raspberry Pi.
Learning multi-planar scene models in multi-camera videos

Many man-made environments are constructed with multiple levels where people walk, joined by stairs, ramps and overpasses. This study proposes a novel method to learn the geometry of a scene containing more than a single ground plane by tracking pedestrians and combining information from multiple views. The method estimates a scene model with multiple planes by measuring the variation of pedestrian heights across each camera's field of view. It segments the image into separate plane regions, estimating the relative depth and altitude for each image pixel, thus building a three-dimensional reconstruction of the scene. By estimating the multiple planes, the method enables tracking algorithms to follow objects (pedestrians and/or vehicles) that are moving on different ground planes in the scene. The authors also introduce what they believe is the first public dataset with pedestrian traffic on multiple planes to encourage other researchers to compare their work in this field.
Real-time architecture for a large distributed surveillance system

The latest generation of surveillance systems can be categorised as concurrent, distributed and large real-time systems. The most common and well-known approach to design these systems is based on object oriented technology. We present another approach to design an intelligent distributed surveillance system. The approach method is known as "real time network approach" or MASCOT, a design method for designing and implementing large real-time concurrent systems. The basic notion is that the flow of data through the system is controlled solely by a set of concurrent processes. We introduce the fundamental concepts of MASCOT and its possible contributions to the creation of good designs for such intelligent distributed surveillance systems. We finish with the illustration of a distributed real time surveillance system using this approach.
Backgroundless detection of pedestrians in cluttered conditions based on monocular images: a review

The significant progress in visual surveillance has been motivated by the need to emulate some of the human ability to monitor activity in human-made environments, particularly in the contexts of security and safety. The rapid rise in numbers of cameras installed in public and private places makes such automation desirable, at least to reduce CCTV workload. Real-world applications of visual surveillance impose the need of robust real-time solutions, able to deal with a wide range of circumstances and environmental conditions. Conventional approaches work based on what has become known as motion (or change) detection followed by tracking (in single or multiple camera systems). Objects of interest are represented by rectangular blobs and decisions on whether something might be interesting are made on rules or learned patterns of presence and trajectories of such blobs. There is growing interest in looking `inside the box` for applications that are concerned with detailed human activity recognition and with robust detection of people even when image backgrounds change, as is the case of a moving camera. In this study, the authors consider the general problem of robust pedestrian detection irrespective of background, reviewing the state of the art, showing some representative results and suggesting ways forward.
MATCHING GRADIENT DESCRIPTORS WITH TOPOLOGICAL CONSTRAINTS TO CHARACTERISE THE CROWD DYNAMICS

Understanding complex crowd scenes involves many dimensions: level of clutter, density of pedestrians, global/detail dynamics of the scene, etc. Socio-dynamics has tackled the problem by providing prototypes of crowd behaviour based on human observations and then explaining them by certain physical models. We are interested in automatic learning the factors involved in complex scenes for different dimensions and deriving a physical model from the real world via computer vision methods. In this paper we propose a solution to extract the motion of complex crowd which could be very difficult for conventional trackers. The method is based on a matching of local gradient descriptor supported by local topology constraints. Spatial pyramid and temporal smoothing are employed for optimizing the algorithm. Dynamics can then be accumulated over time so as to derive a higher level understanding of the scene.
Motion-based machine vision techniques for the management of large crowds

The early detection and the prevention of crowd-related emergencies are the main aims of large crowd management Closed Circuit Television (CCTV) systems. However, environmental and psychological factors play a role in degrading the performance of CCTV operators, hence causing a threat to crowd safety. We present computer vision techniques that estimate the paths and directions of crowd flows in CCTV images and improve the perception of scene dynamics by offering online illustrations. Moreover, we present motion-based algorithms to detect crowd-related emergencies and assist CCTV operators in ensuring crowd safety.
MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods

This paper describes a body of multicamera humanaction video data with manually annotated silhouette datathat has been generated for the purpose of evaluatingsilhouette-based human action recognition methods. Itprovides a realistic challenge to both the segmentationand human action recognition communities and can act asa benchmark to objectively compare proposed algorithms.The public multi-camera, multi-action dataset is animprovement over existing datasets (e.g. PETS, CAVIAR,soccerdataset) that have not been developed specificallyfor human action recognition and complements otheraction recognition datasets (KTH, Weizmann, IXMAS,HumanEva, CMU Motion). It consists of 17 action classes,14 actors and 8 cameras. Each actor performs an actionseveral times in the action zone. The paper describes thedataset and illustrates a possible approach to algorithmevaluation using a previously published action simplerecognition method. In addition to showing an evaluationmethodology, these results establish a baseline for otherresearchers to improve upon.
PRISMATICA: toward ambient intelligence in public transport environments

On-line surveillance to improve safety and security is a major requirement for the management of public transport networks and other public places. The surveillance task is a complex one involving people, management procedures, and technology. This work describes an architecture that takes into account the distributed nature of the detection processes and the need to allow for different types of devices and actuators. This was part of a major European initiative on intelligent transport systems. Because of the dominant nature of closed circuit television in surveillance, This work describes in detail a computer-vision module used in the system and its particular ability to detect situations of interest in busy conditions. The system components have been implemented, integrated, and tested in real metropolitan railway environments and are considered to be the first step toward providing ambient intelligence in such complex scenarios. Results are presented that not only deal with detection performance, but also on the perception of people who used the system on its effectiveness and potential impact.
Multi-view Human Action Recognition Using Histograms of Oriented Gradients (HOG) Description of Motion History Images (MHIs)

In this paper, a silhouette-based view-independent human action recognition scheme is proposed for multi-camera dataset. To overcome the high-dimensionality issue, incurred due to multi-camera data, the low-dimensional representation based on Motion History Image (MHI) was extracted. A single MHI is computed for each view/action video. For efficient description of MHIs Histograms of Oriented Gradients (HOG) are employed. Finally the classification of HOG based description of MHIs is based on Nearest Neighbor (NN) classifier. The proposed method does not employ feature fusion for multi-view data and therefore this method does not require a fixed number of cameras setup during training and testing stages. The proposed method is suitable for multi-view as well as single view dataset as no feature fusion is used. Experimentation results on multi-view MuHAVi-14 and MuHAVi-8 datasets give high accuracy rates of 92.65% and 99.26% respectively using Leave-One-Sequence-Out (LOSO) cross validation technique as compared to similar state-of-the-art approaches. The proposed method is computationally efficient and hence suitable for real-time action recognition systems.
Consensus-based distributed estimation for target tracking in heterogeneous sensor networks

In this paper the problem of distributed target tracking is considered. A network of heterogeneous sensing agents is used to observe a maneuvering target and, at each iteration, all the agents are able to agree about the estimate of the target position, despite the fact that only a small percentage of agents can sense the target at each time instant. Our Consensus-based Distributed Target Tracking (CDTT) is a fully distributed iterative tracking algorithm, in which each iteration is based on two phases: an estimation phase and a consensus one. As a result, the estimated trajectories are identical for all the agents at each time instant. Numerical simulations and comparison with another target tracking algorithm are carried out to show the effectiveness and feasibility of our approach.
Analysis of indoor environments by range images

In this paper we present a reliable method to derive the differences between indoor environments using the comparison of high-resolution range images. Samples belonging to different acquisitions are firstly reduced preserving the topology of the scenes and then registered in the same system of reference through an iterative least-squares algorithm, aided by a deletion mask, whose assignment is the removal of implicit errors due to the different points of view of each orthographic acquisition. Finally the analysis of the exact range measures returns an intuitive difference map that allows the fast detection of the positions of the altered regions within the scenes. Numerical experiments are presented to prove the capability of the method for the comparison of scenes regardless the resolution of the sensor and the input noise level of such measurements.
A distributed heterogeneous sensor network for tracking and monitoring

Distributed networks of sensors have been recognized to be a powerful tool for developing fully automated systems that monitor environments and human activities. Nevertheless, problems such as active control of heterogeneous sensors for high-level scene interpretation and mission execution are open. This paper presents the authors' ongoing research about design and implementation of a distributed heterogeneous sensor network that includes static cameras and multi-sensor mobile robots. The system is intended to provide robot-assisted monitoring and surveillance of large environments. The proposed solution exploits a distributed control architecture to enable the network to autonomously accomplish general-purpose and complex monitoring tasks. The nodes can both act with some degree of autonomy and cooperate with each other. The paper describes the concepts underlying the designed system architecture and presents the results obtained working on its components, including some simulations performed in a realistic scenario to validate the distributed target tracking algorithm.
Using fuzzy RFID modelling and monocular vision for mobile robot global localization

In this paper, we present a mobile robot global positioning system which combines passive RFID and monocular vision. The method is intended to solve the so-called kidnapped robot problem, that is, the problem of estimating the robot pose when it is first booted up or it has been kidnapped and taken to some unknown location. The main novelty of the approach is the use of fuzzy logic to cope with both uncertainty in RFID data and missing tag position information. Fuzzy logic allows us to manage inaccurate sensor data using rules easily understandable by humans. An additional advantage of the proposed method is that it does not require the robot to move around to solve ambiguities, nor does it need several tags to be properly arranged. Conversely, only one passive RFID tag and a single visual landmark are sufficient to univocally estimate the pose of the robot in the environment.
A ball detection algorithm for real soccer image sequences

A large number of methods for circle detection has been studied in the last years for numerous image processing applications. The application domain considered in this paper is the soccer game. To identify the ball in soccer images is very important in order to evaluate the goal event. This domain is challenging as a great number of problems has to be managed, such as occlusions, shadows, objects similar to the ball, real time processing. The aim of this work is to present the results of a number of experiments obtained by using a modified version of the directional circle Hough transform. Different lighting conditions have been considered since they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light the ball appears as a spherical cap then the search of the ball has been modified in order to manage those situations. A large number of experiments has been carried out showing that the proposed method obtains an high detection score.
Explicit knowledge distribution in an omnidirectional distributed vision system

This paper presents an omnidirectional distributed vision system that learns to navigate a robot in an office-like environment without any knowledge about the calibration of the cameras or the robot control law. The system is composed of several omnidirectional vision agents (implemented with an omnidirectional camera and a computer). The first vision agent learns to control the robot with SARSA(/spl lambda/) reinforcement learning, using the LEM strategy to speed-up learning. Once the first vision agent learnt the correct policy, it transfers its knowledge to the other vision agents. The other vision agents might have different intrinsic and extrinsic camera parameters (that are unknown), so a certain amount of re-learning is needed. Reinforcement learning is well suited for this. In this paper, we present the structure of the learning system and the discussion about the optimal values for the learning parameters. During the experimentation the learning phase of the first agent has been carried out, then the knowledge propagation and the re-learning stage of three different agents have been tested. The experimental results demonstrate the feasibility of the approach and the possibility to port the system on the actual robot and cameras.
Multi-Sensor Surveillance ofIndoor Environments by anAutonomous Mobile Robot

In this paper, we present our autonomous mobile robotic system for surveillance of indoor environments. The robot is equipped with a monocular camera, a laser scanner, encoders, and an RFID device, connected to a multi-layer decision and control scheme. Two main functions are implemented: to build a map of the environment, identifying specific areas of interest, marked by RFID tags; to monitor the target zones to detect unexpected changes, such as object addition or removal, based on a vision scene change detector (V-SCD) and a laser scene change detector (L-SCD). Fuzzy logic is used to integrate information provided by the different sensors. Applications of the proposed system include surveillance of wide areas, such as airports and museums, and monitoring of safety equipment. The feasibility of the approach is demonstrated through experimental tests, performed in the ISSIA Mobile Robotics Laboratory of Ban, Italy. The results are promising, proving the method to be effective in detecting either new or removed objects in the surveyed scene, also in presence of relatively small viewpoint changes. It is shown that the proposed robotic surveillance system successfully addresses a number of specific problems related to environment mapping, autonomous navigation, and scene processing, and can be potentially employed in real world surveillance applications.
An algorithm for real time eye detection in face images

The problem of eye detection in face images is very important for a large number of applications ranging from face recognition to gaze tracking. In this paper, we propose a new algorithm for eyes detection that uses iris geometrical information for determining in the whole image the region candidate to contain an eye, and then the symmetry for selecting the couple of eyes. The novelty of this work is that the algorithm works on complex images without constraints on the background, skin color segmentation and so on. Different experiments, carried out on images of subjects with different eyes colors, some of them wearing glasses, demonstrate the effectiveness and robustness of the proposed algorithm.
Visual recognition of missing fastening elements for railroad maintenance

Rail inspection is very important for ensuring safety and preventing dangerous situations. Track super-structure can have different types of anomalies such as defects of rail surface and sleepers, missing of fastening elements and deviations in the contour of the ballast. In this paper we present a vision-based technique for automatically detecting the absence of the fastening bolts that secure the rails to the sleepers. The inspection system uses images from a digital line scan camera installed under a train. The images are preprocessed by using several combinations of wavelet transform and principal component analysis methods. Two different types of classifiers analyse the images in order to evaluate the preprocessing technique that gives the highest rate in detecting the presence of the bolts. The final detecting system (the best combination preprocessing technique and classifier) was applied on a long sequence of real images showing a high reliability and robustness. Results in terms of the detection rate and false positive rate are given in the paper.
Self-location of a mobile robot with uncertainty by cooperation of a heading sensor and a CCD TV camera

Goal-oriented navigation of a mobile robot by landmark based techniques is a straightforward and suitable approach. Most of the methods would normally give an estimation of the position and orientation of the vehicle, but often they are not able to provide a good estimate of the uncertainty in the measurement. That information is useful in application where multisensor fusion is requested. Our method permits to determine the vehicle location and relative uncertainty, when its orientation is obtained by a heading sensor, using a visual landmark based method. The approach is straightforward and suitable for real time performance on general purpose hardware. Experimental results are provided by implementation on our autonomous mobile vehicle SAURO.
Distributed target tracking for sensor networks with only local communication

In this paper the problem of distributed target tracking is considered. A network of agents is used to observe a mobile target and, at each iteration, all the agents agree about the estimate of the target position, despite the fact that they only have local interactions and only a small percentage of them can sense the target. The proposed approach, named Consensus-based Distributed Target Tracking (CDTT), is a fully distributed iterative tracking algorithm. At each iteration our method applies two phases. During the perception phase the target position is obtained either as a measure or as a prediction; subsequently, in the consensus phase a consensus algorithm is applied in order to let all the agents agree on the target position. As a result, the estimated trajectories are identical for all the agents. Numerical simulations are carried out to show the effectiveness and feasibility of our approach.