nils-herrmann/clean_scirepeval huggingface dataset:
Adaptive object classification in surveillance system by exploiting scene context

Surveillance system involving hundreds of cameras becomes very popular. Due to various positions and orientations of camera, object appearance changes dramatically in different scenes. Traditional appearance based object classification methods tend to fail under these situations. We approach the problem by designing an adaptive object classification framework which automatically adjust to different scenes. Firstly, a baseline object classifier is applied to specific scene, generating training samples with extracted scene-specific features (such as object position). Based on that, bilateral weighted LDA is trained under the guide of sample confidence. Moreover, we propose a Bayesian classifier based method to detect and remove outliers to cope with contingent generalization disaster resulted from utilizing high confidence but incorrectly classified training samples. To validate these ideas, we realize the framework into an intelligent surveillance system. Experimental results demonstrate the effectiveness of this adaptive object classification framework.
Robust Multi-resolution Pedestrian Detection in Traffic Scenes

The serious performance decline with decreasing resolution is the major bottleneck for current pedestrian detection techniques. In this paper, we take pedestrian detection in different resolutions as different but related problems, and propose a Multi-Task model to jointly consider their commonness and differences. The model contains resolution aware transformations to map pedestrians in different resolutions to a common space, where a shared detector is constructed to distinguish pedestrians from background. For model learning, we present a coordinate descent procedure to learn the resolution aware transformations and deformable part model (DPM) based detector iteratively. In traffic scenes, there are many false positives located around vehicles, therefore, we further build a context model to suppress them according to the pedestrian-vehicle relationship. The context model can be learned automatically even when the vehicle annotations are not available. Our method reduces the mean miss rate to 60% for pedestrians taller than 30 pixels on the Caltech Pedestrian Benchmark, which noticeably outperforms previous state-of-the-art (71%).
Face liveness detection using 3D structure recovered from a single camera

Face recognition, which is security-critical, has been widely deployed in our daily life. However, traditional face recognition technologies in practice can be spoofed easily, for example, by using a simple printed photo. In this paper, we propose a novel face liveness detection approach to counter spoofing attacks by recovering sparse 3D facial structure. Given a face video or several images captured from more than two viewpoints, we detect facial landmarks and select key frames. Then, the sparse 3D facial structure can be recoveredfrom the selected key frames. Finally, an Support Vector Machine (SVM) classifier is trained to distinguish the genuine and fake faces. Compared with the previous works, the proposed method has the following advantages. First, it gives perfect liveness detection results, which meets the security requirement of face biometric systems. Second, it is independent on cameras or systems, which works well on different devices. Experiments with genuine faces versus planar photo faces and warped photo faces demonstrate the superiority of the proposed method over the state-of-the-art liveness detection methods.
Illumination and motion-based video enhancement for night surveillance

This work presents a context enhancement method of low illumination video for night surveillance. A unique characteristic of the algorithm is its ability to extract and maintenance the meaningful information like highlight area or moving objects with low contrast in the enhanced image, meanwhile recover the surrounding scene information by fusing the daytime background image. A main challenge comes from the extraction of meaningful area in the night video sequence. To address this problem, a novel bidirectional extraction approach is presented. In evaluation experiments with real data, the notable information of the night video is extracted successfully and the background scene is fused smoothly with the night images to show enhanced surveillance video for observers.
Structural models for face detection

Despite the success in the last two decades, the state-of-the-art face detectors still have problems in dealing with images in the wild for the large appearance variations. Instead of taking appearance variations as black boxes and leaving them to statistical learning algorithms, we propose a structural face model to explicitly represent them. Our hierarchical part based structural face model enables part subtype option to describe appearance variations of the local part, and part deformation to capture the deformable variations between different poses and expressions. In the process of detection, the input candidate is first fitted by the structural model to infer the part location and part subtype, and the confidence score is then computed based on the fitted configuration to reduce the influence of structure variation. Besides the face model, we utilize the co-occurrence of face and body to further boost the face detection performance. We present a method for training phrase based body detectors, and propose a structural context model to jointly use the results of face detector and various body detectors. Experiments on the challenging FDDB show that our method has state-of-the-art performance, compared with other commercial and academic systems.
Moving Object Detection Revisited: Speed and Robustness

The detection of moving objects in videos is very important in many video processing applications, and background modeling is often an indispensable process to achieve this goal. Most of the traditional background modeling methods utilize color or texture information. However, color information is sensitive to illumination variations and texture information cannot be utilized to separate smooth foreground from smooth background in most cases. Achieving good performance in terms of high foreground detection accuracy and low computational cost is also challenging. In this paper, we propose a new integration framework of texture and color information for background modeling, in which the foreground decision equation includes three parts (one part for color information, one part for texture information, and the left part for the integration of color and texture information). This framework is able to combine the advantages of texture and color features while inhibiting their disadvantages as well. Moreover, we propose a block-based method to accelerate the background modeling. In particular, in the texture information modeling process, a single histogram model is established for each block whose bins indicate the occurrence probabilities of different patterns, which is different from the traditional multihistogram model for block-based background modeling, and then dominant background patterns are selected to calculate the background likelihood of new coming blocks. Dynamic background and multimodal problems can be handled through this technique. To evaluate the foreground detection performance reasonably, a new quality measure is proposed. Extensive experiments on various challenging videos validate the effectiveness of the proposed method over state-of-the-art methods.
Half Quadratic Analysis for Mean Shift: with Extension to A Sequential Data Mode-Seeking Method

Theoretical understanding and extension of mean shift procedure has received much attention recently. In this paper, we present a theoretical exploration and an algorithm development on mean shift. In the theory part, we point out that convex profile based mean shift can be justified from the viewpoint of half-quadratic (HQ) optimization. Such analysis facilitates the convergence study and uni-mode bandwidth selection for the latest variation, annealed mean shift. In the algorithm development part of this paper, we extend annealed mean shift inside our HQ framework to a novel method, namely adaptive mean shift (Ada-MS), to detect multiple data modes sequentially from an arbitrary starting point in linear running time. To validate the performance, we couple the investigation with two applications: image segmentation and color constancy. Extensive experiments show that the proposed method is time efficient and initialization invariant.
Multi-label CNN based pedestrian attribute learning for soft biometrics

Recently, pedestrian attributes like gender, age and clothing etc., have been used as soft biometric traits for recognizing people. Unlike existing methods that assume the independence of attributes during their prediction, we propose a multi-label convolutional neural network (MLCNN) to predict multiple attributes together in a unified framework. Firstly, a pedestrian image is roughly divided into multiple overlapping body parts, which are simultaneously integrated in the multi-label convolutional neural network. Secondly, these parts are filtered independently and aggregated in the cost layer. The cost function is a combination of multiple binary attribute classification cost functions. Moreover, we propose an attribute assisted person re-identification method, which fuses attribute distances and low-level feature distances between pairs of person images to improve person re-identification performance. Extensive experiments show: 1) the average attribute classification accuracy of the proposed method is 5.2% and 9.3% higher than the SVM-based method on three public databases, VIPeR and GRID, respectively; 2) the proposed attribute assisted person re-identification method is superior to existing approaches.
Deep Metric Learning for Person Re-identification

Various hand-crafted features and metric learning methods prevail in the field of person re-identification. Compared to these methods, this paper proposes a more general way that can learn a similarity metric from image pixels directly. By using a "siamese" deep neural network, the proposed method can jointly learn the color feature, texture feature and metric in a unified framework. The network has a symmetry structure with two sub-networks which are connected by a cosine layer. Each sub network includes two convolutional layers and a full connected layer. To deal with the big variations of person images, binomial deviance is used to evaluate the cost between similarities and labels, which is proved to be robust to outliers. Experiments on VIPeR illustrate the superior performance of our method and a cross database experiment also shows its good generalization.
Prime Discriminant Simplicial Complex

The structure representation of data distribution plays an important role in understanding the underlying mechanism of generating data. In this paper, we propose the prime discriminant simplicial complex (PDSC) by utilizing persistent homology to capture such structures. Assuming that each class is represented with a prime simplicial complex, we classify unlabeled samples based on the nearest projection distances from the samples to the simplicial complexes. We also extend the extrapolation ability of these complexes with a projection constraint term. Experiments in simulated and practical datasets indicate that, compared with several published algorithms, the proposed PDSC approaches achieve promising performance without losing structure representation.
Subspace learning with frequency regularizer: Its application to face recognition

Subspace learning is an important technique to enhance the discriminative ability of feature representation and reduce the dimension to improve its efficiency. Due to limited training samples and the usual high-dimensional feature, subspace learning always suffers from overfitting problem, which affects its generalization performance. One possible method is to introduce prior information as a regularizer to constrain its solution space. Traditional regularizers are usually designed in spatial domain, which usually make the projection smooth. In this work, we propose a frequency regularizer (FR), which suppresses the high frequency energy so that the smooth priori is incorporated. Two representative supervised subspace methods with frequency regularizer, FR-LDA and FR-SR are introduced and further applied to face recognition problem. Extensive experiments on popular face databases validate the effectiveness and superiority of FR based subspace learning compared to traditional subspace learning methods.
Learning Feature Extraction and Classification for Tracking Multiple Objects: A Unified Framework

A great challenge in tracking multiple objects is how to locate each object when they interact and form a group. We view it as a binary classification problem. It is important to base the classification on the currently most discriminative features. We derive a unified framework for learning feature extraction and classification in appearance-spatial space for multiple object tracking. In this framework, both classifier design and feature evaluation are accomplished by minimizing an criterion which corresponds to an upperbound of classification error. There, the most discriminative features, as variables, minimize the criterion function, whereas the classifier, as a function, minimizes the criterion functional. The resulting system offers high accuracy for real-time tracking of nearby multiple objects in complex and dynamic scenes.
Discriminant analysis with Gabor phase for robust face recognition

This paper presents an occlusion robust image representation method and apply it to face recognition. Inspired from the recent work , we propose a Gabor phase difference representation for occlusion robust face recognition. Based on the good ability of Gabor filters to capture image structure and the robustness to image occlusion shown in this paper, Gabor phase features are expected to be discriminative and robust for face representation in occlusion case. Besides, we adopt spectral regression based discriminant analysis with the extracted Gabor phase features to find the most discriminant subspace to classify different faces. In this way, an occlusion robust face image discriminant subspace is derived. Extensive experiments with various occlusion cases show the efficacy of the proposed method.
Context-Aware Medical Content Adaptation through Semantic Representation and Rules Evaluation

Proper coding and transmission of medical and physiological data is a crucial issue for the effective deployment and performance of telemedicine services. The paper presents a platform for performing proper medical content adaptation based on context awareness. Sensors are used in order to determine the status of a patient being monitored through a medical network. Additional contextual information regarding the patient¿s environment (e.g., location, data transmission device and underlying network conditions, etc.) are represented through an ontological knowledge base model. Rule-based evaluation determines proper content (i.e., biosignals, medical video and audio) coding and transmission of medical data, in order to optimize the telemedicine process. The paper discusses the design of the ontological model and provides an initial assessment.
Towards supporting the teaching of history using an intelligent information system that relies on the electronic road metaphor

The new educational system in Greece, as far as teaching of history is concerned, is characterized by a shift away from sterile memorization and towards a critical approach of historical facts and phenomena that would contribute in both the development of pupils' historical concept and conscience and the promotion of critical thought. This reflects the globally accepted goals of teaching history courses. Such teaching goals can be greatly supported by computer-based applications, which can offer access to vast amounts of historical texts and data to be used next to the main scholar textbook and be analyzed by pupils. Still, existing applications seem to be quite inadequate for this purpose, as they require that the pupil be already informed on a matter, before the initiation of a quest for data. We describe an intelligent information system that is designed to facilitate browsing of educational material and historical sources, thus allowing pupils to efficiently retrieve information on topics that are not yet known to them and expand in this way their historical knowledge. This can help in fulfilling the teaching goals of the new educational system.
Combining synchronous and asynchronous distance learning for adult education: the Greek army case

A major issue in military training is the territorial dispersion of military personnel which enforces officers to be gathered in training camps to attend the lessons. This is extremely costly, as the learners have to move to the training camp and leave their position. The School of Research and Informatics for Officers of the Greek Army in cooperation with the academic community in Greece implemented a pilot computer assisted distance learning programme in Operational Business Management. We describe the results, the experience acquired during the implementation and an overall assessment of the pilot program.
Multimodal Emotion Recognition and Expressivity Analysis

The paper presents the framework of a special session that aims at investigating the best possible techniques for multimodal emotion recognition and expressivity analysis in human computer interaction, based on a common psychological background. The session mainly deals with audio and visual emotion analysis, with physiological signal analysis serving as supplementary to these modalities. Specific topics that are examined include extraction of emotional features and signs from each modality in separate, integration of the outputs of single-mode emotion analysis systems and recognition of the user's emotional state, taking into account emotion models and existing knowledge or demands from both the analysis and synthesis perspective. Various labeling schemes, supply of accordingly labeled test databases, as well as synthesis of expressive avatars and affective interactions, are issues brought up and examined in the proposed framework
THETIS: Three Dimensional Tennis Shots a Human Action Dataset

The detection and classification of human movements, as a joint field of Computer Vision and Pattern Recognition, is used with an increasing rate in applications designed to describe human activity. Such applications require efficient methods and tools for the automatic analysis and classification of motion capture data, which constitute an active field of research. To facilitate the development and the benchmarking of methods for action recognition, several video collections have previously been proposed. In this paper, we present a new video database that can be used for an objective comparison and evaluation of different motion analysis and classification methods. The database contains video clips that capture the 3D motion of individuals. To be more specific, the set consists of 8374 video clips, which contain 12 different types of tennis actions performed by 55 individuals, captured by Kinect. Kinect provides the depth map of motion data and helps to extract the 3D skeletal joint connections. Performing experiments using state of the art algorithms, the database shows to be very challenging. It contains very similar to each other actions, offering the opportunity to algorithms dedicated to gaming and athletics, to be developed and tested. The database is freely available for research purposes.
User modeling via gesture and head pose expressivity features

Current work focuses on user modeling in terms of affective analysis that could in turn be used in intelligent personalized interfaces and systems, dynamic profiling and context-aware multimedia applications. The analysis performed within this work comprises of statistical processing and classification of automatically extracted gestural and head pose expressivity features. Computational formulation of qualitative expressive cues of body and head motion is performed and the resulting features are processed statistically, their correlation is studied and finally an emotion recognition attempt is presented based on these features. Significant emotion specific patterns and expressivity features interrelations are derived while the emotion recognition results indicate that the gestural and head pose expressivity features could supplement and enhance a multimodal affective analysis system incorporating an additional modality to be fused with other commonly used modalities such as facial expressions, prosodic and lexical acoustic features and physiological measurements.
Efficient use of content-based retrieval in quick browsing: a realization

Due to recent growth of interest in multimedia applications, an increasing demand has emerged for efficient storage, management and browsing in multimedia databases. This fact has made the content-based retrieval (CBR) concept very popular during the past decade. However, it has been practically shown that CBR tools respond successfully to the inexperienced user queries very rarely. It is for this reason that in commercial systems, queries are supported by the use of annotations or manual indexing. In this work, content-based query, retrieval and indexing capabilities have been combined with an intelligent agent framework over a simple database architecture. The proposed system is based on the ideas presented in (Xirouhakis et al., 1998) which are implemented and further extended in this work.
The platformer experience dataset

Player modeling and estimation of player experience have become very active research fields within affective computing, human computer interaction, and game artificial intelligence in recent years. For advancing our knowledge and understanding on player experience this paper introduces the Platformer Experience Dataset (PED) - the first open-access game experience corpus - that contains multiple modalities of user data of Super Mario Bros players. The open-access database aims to be used for player experience capture through context-based (i.e. game content), behavioral and visual recordings of platform game players. In addition, the database contains demographical data of the players and self-reported annotations of experience in two forms: ratings and ranks. PED opens up the way to desktop and console games that use video from webcameras and visual sensors and offer possibilities for holistic player experience modeling approaches that can, in turn, yield richer game personalization.
Virtual worlds modeling for Web deployment

We propose a design architecture for the graphics engine of a virtual reality system to be deployed over the Web. The graphics engine is decomposed into high and low level components. The high level component manages the hierarchical organization of the virtual objects, while the low level component acts as the interface to the graphics hardware and is mainly responsible for operations, such as vertex transforms and polygon rasterization. Several methods for geometry representation, compression and culling are compared and associated with different geometry types. The use of OpenGL as a general-purpose machine to implement mathematical operations is considered in the context of programmable shaders. Finally a prototype application for virtual worlds modeling is presented along with detailed discussion on technical and implementation issues.