nils-herrmann/clean_scirepeval huggingface dataset:
Mobile vehicle's egomotion estimation from time varying image sequences

Sparse optical flow estimated by an image sequence, often, is enough to obtain useful information about camera motion. Normally, matching among features on image sequence is estimated by radiometric similarity. When the camera is moving on a flat surface (planer navigation) additional constraints can be introduced in order to obtain a more robust estimation of the optical flow. In this paper, an energy based optimization method to compute a robust sparse optical flow using global constraints induced by camera motion, is proposed. Convergence is reached in few iterations, making the proposed method suitable for real-time applications.
Object detection in images: run-time complexity and parameter selection of support vector machines

We address two aspects related to the exploitation of support vector machines (SVM) for classification in real application domains, such as the detection of objects in images. The first one concerns the reduction of the run-time complexity of a reference classifier without increasing its generalization error. We show that the complexity in test phase can be reduced by training SVM classifiers on a new set of features obtained by using principal component analysis (PCA). Moreover due to the small number of features involved, we explicitly map the new input space in the feature space induced by the adopted kernel function. Since the classifier is simply a hyperplane in the feature space, then the classification of a new pattern involves only the computation of a dot product between the normal to the hyperplane and the pattern. The second issue concerns the problem of parameter selection. In particular we show that the receiver operating characteristic curves, measured on a suitable validation set, are effective for selecting, among the classifiers the machine implements, the one having performances similar to the reference classifier. We address these two issues for the particular application of detecting goals during a football match.
People re-identification and tracking from multiple cameras: A review

People tracking is a central and crucial point for the development of intelligent surveillance systems. When multiple cameras are used, the problem becomes more challenging as people re-identification is needed. Humans can greatly change their appearance according to posture, clothing and lighting conditions, thus defining features that describe people moving in large scenarios is a complex task. In this paper the problem of people re-identification and tracking is reviewed. The most used methodologies are discussed and insight into open problems and future research directions is provided.
A 3D vision system for high resolution surface reconstruction

In this paper a fast and innovative three-dimensional vision system, having high resolution in the surface reconstruction, is discussed. It is based on a triangulation 3D laser scanner with a linear beam shape. The high precision (few microns) is guaranteed by very small laser line width, small camera pixel-size and proper optical properties of the Telecentric Lens. The entire system has been tested on two kinds of sample objects such as a 20 €cent coin and a set of precision drilling tools. The main purpose of this work is the detection and reconstruction of the 3D surface of tiny objects and the measurement of their surface defects with high accuracy. Furthermore the occlusion problem is faced and solved by properly handling the camera-laser setup. Experimental tests prove the high precision of the system that can reach a resolution of 15 μm.
Supervised learning of RFID sensor model using a mobile robot

RFID sensor modelling has been recognized as a fundamental step towards successful application of RFID technology in mobile robotics tasks, such as localization and environment mapping. In this paper, we propose a novel approach to passive RFID modelling, using fuzzy reasoning. Specifically, the RFID sensor model is defined as a combination of an RSSI model and a Tag Detection Model, both of which are learnt based on an Adaptive Neuro Fuzzy Inference System (ANFIS). Fuzzy C-Means (FCM) algorithm is applied to automatically cluster sample data into classes and obtain initial data memberships for ANFIS initialization and training. Experimental results from tests performed in our Mobile Robotics Lab are presented, showing the effectiveness of the proposed method.
A fuzzy logic approach to Passive RFID for mobile robot applications

Passive Radio Frequency Identification (RFID) is being increasingly used in mobile robotics applications, as it provides inexpensive and effective solutions to data association issues in basic navigation tasks. Nonetheless, problems related to sensitivity of the signal to interference and reflections, and missing tag range and bearing information are open. In this paper, we propose a novel approach to passive RFID, which tackles those issues using fuzzy reasoning. Specifically, first, we present a fuzzy antenna model. Then, based on this model, we describe two fuzzy logic methods for tag localization. One allows us to accurately localize passive tags in the environment and to generate what we call an RFID-augmented map; the other is suited for estimating the bearing of a tag relative to the robot. The general use of both methods is in object localization, map building, environment monitoring, and robot pose estimation. Results of experimental tests demonstrate that fuzzy logic is appropriate to operate under uncertainty in RFID systems, and allows for accurate tag localization.
Complexity reduction and parameter selection in support vector machines

We focus on two central issues regarding the application of support vector machines (SVM) for classification in real contexts. The first one deals with the reduction of the computational complexity of a reference classifier during the test phase, without increasing the generalization error of the classifier. In particular, we show that the complexity in the test phase can be reduced by training SVM classifiers on a new set of uncorrelated features obtained by using the principal component analysis and explicitly mapping the new input space in the feature space induced by the adopted kernel function. The second issue deals with the problem of parameter selection. In particular we show that the receiver operating characteristic curves, measured on a suitable validation set, are effective for selecting among the classifiers the machine implements the one having performances similar to the reference classifier. We address these two issues in the context of object detection in images and in particular for the specific problem of detecting goal during a football match.
Visual navigation of a mobile robot integrating an heading sensor

Visual navigation is the ability of a mobile robot to determine its position (relative or absolute) in the environment using visual sensors such as TV cameras. A goal-oriented autonomous mobile robot is a vehicle able to plan a path from its current position to a predefined goal and to navigate on the planned path, determining continuously its location, in order to verify if the route is correctly followed. We propose a visual self-location method to determine the vehicle location, based on cooperation of an heading sensor and a CCD TV camera. 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.
Neural Q-learning control architectures for a wall-following behavior

The Q-learning algorithm, for its simplicity and well-developed theory, has been largely used in the last years in order to realize different behaviors for autonomous vehicles. The most frequent applications required the standard tabular formulation with discrete sets of state and action. In order to consider continues variables, function approximators such as neural networks are required. In this work we investigate the neural approach of Q-learning on the robot navigation task of wall following. Some issues have been addressed in order to deal with the convergence problem and the need of huge training sets. The experience replay paradigm has been also applied to reduce the unlearning problem. Two different neural network architectures, which use different spatial decompositions of the sensory input, have been compared. The aim is to investigate how different choices of architecture can affect the learning convergence, the optimality of the final controller and the generalization ability.
Goal detection in football by using support vector machines for classification

We present a technique for detecting goals during a football match by using images acquired by a single camera placed externally to the field. The method does not require the modification neither of the ball nor of the goalmouth. Due to the attitude of the camera with respect to the football ground, the system can be thought of as an electronic linesman which helps the referee in establishing the occurrence of a goal during a football match. The occurrence of the event is established by detecting the ball and comparing its position with respect to the location of the goalpost in the image. The ball detection technique relies on a supervised learning scheme called support vector machines for classification. The examples used for training are appropriately filtered version of views of the object to be detected, previously stored in the form of image patterns. We have extensively tested the technique on real images in which the ball is both fully visible and partially occluded. The performance of the proposed detection scheme are measured in terms of detection rate, false positive rate and precision in the ball localisation in image.
RFID-based environment mapping for autonomous mobile robot applications

Radio Frequency Identification (RFID) is being increasingly used as an augmentation technology in the domain of environment mapping and ubiquitous computing. In this paper, we present a novel method for localizing RFID tags embedded in indoor environments, by using a mobile robot equipped with RF antennas and reader, and a laser range finder. First, a model of the RFID system is learnt, which describes the likelihood of detecting a tag, given its position relative to the robot. Then, based on this model, a fuzzy inference method is developed that allows to localize tags in the environment. Tag locations are referred to a map of the environment, which is obtained from laser range data. Results of experimental tests show that the proposed approach is accurate in localizing RFID tags and can be successfully integrated with autonomous navigation and mapping systems.
Visual state recognition for a target-reaching task

We present a learning algorithm that realizes a simple goal-reaching task for an autonomous vehicle when only visual information about the goal is provided. The robot has to reach a door from every position of the environment. The state of the system is based on visual information received by a TV camera placed on the mobile robot. The vision algorithm is able to determine the relative position between the vehicle and the door according to the slopes of the contour lines of the door. A learning phase is carried out in simulation to obtain the optimal state-action rules. The learned knowledge is then transferred on the real robot for the testing phase. Experimental results show the generality of the knowledge learned as the real robot is always able to execute its paths towards the door in different environments.
Improving performance of an omnidirectional range sensor for 3D modeling of environments

High resolution in distance (range) measurements can be achieved by means of accurate instrumentations and precise analytical models. This paper reports an improvement in the estimation of distance measurements performed by an omnidirectional range sensor already presented in literature. This sensor exploits the principle of laser triangulation, together with the advantages brought by catadioptric systems, which allow the reduction of the sensor size without decreasing the resolution. Starting from a known analytical model in two dimensions (2D), the paper shows the development of a fully 3D formulation where all initial constrains are removed to gain in measurement accuracy. Specifically, the ray projection problem is solved by considering that both the emitter and the receiver have general poses in a global system of coordinates. Calibration is thus made to estimate their poses and compensate for any misalignment with respect to the 2D approximation. Results prove an increase in the measurement accuracy due to the more general formulation of the problem, with a remarkable decrease of the uncertainty.
Building low cost vehicles for simple reactive behaviors

We describe how it is possible to build low cost autonomous vehicles by using only simple commercial electronic devices and toys. As suggested by Braitenberg (1984) in his experiments of synthetic psychology, we build two simple and compact vehicles, each capable of doing two different basic behaviors: the wall following (moving the vehicle at a constant distance from the wall on its left or right hand) and the obstacle avoidance (avoiding collision with obstacles during the path). Two different control systems have been developed and compared. The first system is controlled by an analogical circuit, whereas the second by a digital circuit. The main purpose of our work is to develop platforms for helping students to control and manipulate robots and computers. Following this activity children can learn by doing and, most of all, in a playful way.
Position estimation for a mobile robot using data fusion

This paper describes a position estimation technique based on the fusion of data obtained by two independent subsystems in a mobile robot navigation context. The first subsystem is a self-location one composed of an onboard camera, an onboard image processing unit and artificial landmarks; the second one is a dead-reckoning subsystem based on odometry. The robot navigation system integrates the position estimation obtained by the vision subsystem with the position estimated by odometry using a Kalman filter framework.
Learning actions from vision-based positioning in goal-directed navigation

We describe a navigation approach, based on a reinforcement learning algorithm, that allows a mobile robot to move in an unknown indoor environment learning autonomously in a few trials the actions for reaching a particular goal location. The control architecture merges visual information and sonar readings to evaluate the state of the system to which the learning algorithm relates the best movement for reaching the goal. As a result, after limited experience, the robot learns efficient behavioral sequences and improves its learning incrementally. In addition the learning system adapts well to new situations of the environment or new task requirements. The results obtained in simulation and in real experiments, carried out in our laboratory, have shown that our system is tolerant to noise in sensor measurements and during each trial is always able to reach the goal.
Spatiotemporal latent semantic cues for moving people tracking

Effective and robust visual tracking is one of the most important tasks for the intelligent visual surveillance. In this paper, we proposed a novel method for detecting and tracking moving people using the spatiotemporal latent semantic cues and the incremental eigenspace tracking techniques. During tracking process, the target appearance model is incrementally learned in low dimensional tensor eigenspace by adaptively updating the eigenbasis and sample mean. At the same time, the spatiotemporal latent semantic cues calibrate the estimation of tracking and detect new moving people coming in the same surveillance scene. Experiment results show that with the calibration based on spatiotemporal latent semantic cues, the proposed method can track the moving people automatically and effectively.
Application Potential of Multimedia Information Retrieval

This paper will first briefly survey the existing impact of multimedia information retrieval (MIR) in applications. It will then analyze the current trends of MIR research which can have an influence on future applications. It will then detail the future possibilities and bottlenecks in applying the MIR research results in the main target application areas, such as the consumer (e.g., personal video recorders, web information retrieval), public safety (e.g., automated smart surveillance systems), and professional world (e.g., automated meeting capture and summarization). In particular, recommendations will be made to the research community regarding the challenges that need to be met to make the knowledge transfer towards the applications more efficient and effective. It will also attempt to study the trends in the applications which can inform the MIR community on directing intellectual resources towards MIR problems which can have a maximal real-world impact.
Render Sequence Encoding for Document Protection

We present in this paper a novel electronic document watermarking method, render sequence encoding (RSE), and then further develop a RSE authentication method for electronic documents. RSE watermarks an electronic document by modulating the display sequences of words or characters. It features large information-carrying capacity and robustness over document format transcoding. The RSE authentication method is based on the NP-complete exact traveling salesman problem, which provides a rigorous foundation for security. The RSE authentication method is secure in the sense it is extremely difficult to forge the authentication process. RSE authentication process is also easy to operate, especially in comparison to digital signatures which requires public key infrastructure for its operation
Music synthesis for home videos: an analogy based approach

There have been efforts in the recent years to make home videos look more interesting and pleasing to viewers by mixing it with music. Most of the existing software enables the user to add music of his preference. It assumes that the user has enough knowledge about the aesthetic mixing principles. In our research, we propose a way of adding audio to video by synthesizing appropriate music based on the video content. This is a step toward semi-automatic music mixing without totally excluding the user. We have developed a system that takes in music examples selected by the user and generates new music by applying the aesthetic rules of audio-video mapping. The paper concentrates on pitch generation for music synthesized through contour based pitch matching by using technique of string matching. This system would enable the user to understand what music would be suited to a particular video and would assist him in choosing a matching piece of music.
Privacy modeling for video data publication

Video cameras are being extensively used in many applications. Huge amounts of video are being recorded and stored everyday by surveillance systems. Any proposed application of this data raises severe privacy concerns. An assessment of privacy loss is necessary before any potential application of the data. In traditional methods of privacy modeling, researchers have focused on explicit means of identity leakage like facial information, etc. However, other implicit inference channels through which individual's an identity can be learned have not been considered. For example, an adversary can observe the behavior, look at the places visited and combine that with the temporal information to infer the identity of the person in the video. In this work, we thoroughly investigate privacy issues involved with the video data considering both implicit and explicit channels. We first establish an analogy with the statistical databases and then propose a model to calculate the privacy loss that might occur due to publication of the video data. The experimental results demonstrate the utility of the proposed model.
Erasing video logos based on image inpainting

A video logo is usually a declaration of the video copyright. However it sometimes causes visual discomfort due to the presence of multiple logos in videos that have been filed and exchanged by different channels. We present an approach to erase logos from video clips. Based on the histogram energy analysis of the relevant video frames, we obtain the best quality logo frame that can be easily processed in the selected region of video frames. After that, we mark the logo area in the entire sequence of frames and inpaint each frame of the video logo based on color interpolation. We describe our technique and also provide experimental results.
Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition

This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and sparsity with respect to two latent variables, model parameters and grouping information, for joint optimization. To handle this non-convex optimization, we decompose it into two sub-tasks, multi-task learning and task relatedness discovery. First, we convert this non-convex objective function into the convex formulation by fixing the latent grouping information. This new objective function focuses on multi-task learning by strengthening the shared-action relationship and action-specific feature learning. Second, we leverage the learned model parameters for the task relatedness measure and clustering. In this way, HC-MTL can attain both optimal action models and group discovery by alternating iteratively. The proposed method is validated on three kinds of challenging datasets, including six realistic action datasets (Hollywood2, YouTube, UCF Sports, UCF50, HMDB51 <inline-formula> <tex-math notation="LaTeX">$\&$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq1-2537337.gif"/> </alternatives></inline-formula> UCF101), two constrained datasets (KTH <inline-formula><tex-math notation="LaTeX"> $\&$</tex-math><alternatives><inline-graphic xlink:href="liu-ieq2-2537337.gif"/></alternatives></inline-formula> TJU), and two multi-view datasets (MV-TJU <inline-formula><tex-math notation="LaTeX">$\&$</tex-math><alternatives> <inline-graphic xlink:href="liu-ieq3-2537337.gif"/></alternatives></inline-formula> IXMAS). The extensive experimental results show that: 1) HC-MTL can produce competing performances to the state of the arts for action recognition and grouping; 2) HC-MTL can overcome the difficulty in heuristic action grouping simply based on human knowledge; 3) HC-MTL can avoid the possible inconsistency between the subjective action grouping depending on human knowledge and objective action grouping based on the feature subspace distributions of multiple actions. Comparison with the popular clustered multi-task learning further reveals that the discovered latent relatedness by HC-MTL aids inducing the group-wise multi-task learning and boosts the performance. To the best of our knowledge, ours is the first work that breaks the assumption that all actions are either independent for individual learning or correlated for joint modeling and proposes HC-MTL for automated, joint action grouping and modeling.
Applying neural network on the content-based audio classification

Many audio and multimedia applications would benefit if they could interpret the content of audio rather than relying on descriptions or keywords. These applications include multimedia databases and file systems, digital libraries, automatic segmentation or indexing of video (e.g., news or sports storage), and surveillance. This paper describes a novel content-based audio classification approach based on neural network and genetic algorithm. Experiments show this approach achieves a good performance of the classification.