nils-herrmann/clean_scirepeval huggingface dataset:
A bit-rate control method in video communication

Bit-rate control is one of the most important issues in video communication. We introduce PID into the picture layer bit-rate control in H.263+ application, which is characterized by its open structure. The combination of PID feedback control and model-based TMN8 bit-rate control method will improve the adaptability of the rate controller. This method will scale-down the effect of random disturbance and the error caused by the variance between the real system and its model. Furthermore, it applies to both constant bit-rate (CBR) control and variable bit-rate (VBR) control.
A new wireless sensor network middleware for collaborative object tracking

Due to the overall complexity of wireless sensor networks, programming sensor networks poses considerable impediments and makes application development nontrivial. A middleware layer is a novel approach to glue together the operating systems, network stacks, and applications. In this paper, a new wireless sensor network middleware for collaborative object tracking has been proposed, which is consisted of three related algorithms: the node selection algorithm, the object management algorithm, and the session maintenance algorithm. The simulation results using ns-2 show that in a wireless sensor network using IEEE 802.15.4 protocol, the system with the proposed middleware has better performance than system without the proposed middleware.
Contour-enhanced resampling of 3D point clouds via graphs

To reduce storage and computational cost for processing and visualizing large-scale 3D point clouds, an efficient resampling strategy is needed to select a representative subset of 3D points that can preserve contours in the original 3D point cloud. We tackle this problem by using graph-based techniques as graphs can represent underlying surfaces and lend themselves well to efficient computation. We first construct a general graph for a 3D point cloud and then propose a graph-based metric to quantify the contour information via high-pass graph filtering. Finally, we obtain an optimal resampling distribution that preserves the contour information by solving an optimization problem. When browsing, the proposed graph-based resampling performs better than uniform resampling both for toy point clouds as well as real large-scale point clouds. Furthermore, as neither mesh construction nor surface normal calculation is involved, the proposed graph-based method is computationally more efficient than the mesh-based methods.
A joint estimation of head and body orientation cues in surveillance video

The automatic analysis and understanding of behavior and interactions is a crucial task in the design of socially intelligent video surveillance systems. Such an analysis often relies on the extraction of people behavioral cues, amongst which body pose and head pose are probably the most important ones. In this paper, we propose an approach that jointly estimates these two cues from surveillance video. Given a human track, our algorithm works in two steps. First, a per-frame analysis is conducted, in which the head is localized, head and body features are extracted, and their likelihoods under different poses is evaluated. These likelihoods are then fused within a temporal filtering framework that jointly estimate the body position, body pose and head pose by taking advantage of the soft couplings between body position (movement direction), body pose and head pose. Quantitative as well as qualitative experiments show the benefit of several aspects of our approach and in particular the benefit of the joint estimation framework for tracking the behavior cues. Further analysis of behavior and interaction could then be conducted based on the output of our system.
Klewel Webcast: From Research to Growing Company

The main motivations behind the webcasting company, Klewel, were to go beyond text-based conference proceedings to offer a user-friendly way to browse and replay presentations, access slides, and search for specific information within a recording. As the project got under way, the team came together, and eventually an innovative webcasting solution saw the light of day. Learn how the company moved from research to technology by focusing on multidisciplinary research, early real-world prototyping and testing, collaboration, and client needs.
Short-Term Spatio–Temporal Clustering Applied to Multiple Moving Speakers

Distant microphones permit to process spontaneous multiparty speech with very little constraints on speakers, as opposed to close-talking microphones. Minimizing the constraints on speakers permits a large diversity of applications, including meeting summarization and browsing, surveillance, hearing aids, and more natural human-machine interaction. Such applications of distant microphones require to determine where and when the speakers are talking. This is inherently a multisource problem, because of background noise sources, as well as the natural tendency of multiple speakers to talk over each other. Moreover, spontaneous speech utterances are highly discontinuous, which makes it difficult to track the multiple speakers with classical filtering approaches, such as Kalman filtering of particle filters. As an alternative, this paper proposes a probabilistic framework to determine the trajectories of multiple moving speakers in the short-term only, i.e., only while they speak. Instantaneous location estimates that are close in space and time are grouped into ldquoshort-term clustersrdquo in a principled manner. Each short-term cluster determines the precise start and end times of an utterance and a short-term spatial trajectory. Contrastive experiments clearly show the benefit of using short-term clustering, on real indoor recordings with seated speakers in meetings, as well as multiple moving speakers.
Embedding motion in model-based stochastic tracking

Particle filtering (PF) is now established as one of the most popular methods for visual tracking. Within this framework, two assumptions are generally made. The first is that the data are temporally independent given the sequence of object states, and the second one is the use of the transition prior as proposal distribution. In this paper, we argue that the first assumption does not strictly hold and that the second can be improved. We propose to handle both modeling issues using motion. Explicit motion measurements are used to drive the sampling process towards the new interesting regions of the image, while implicit motion measurements are introduced in the likelihood evaluation to model the data correlation term. The proposed model allows to handle abrupt motion changes and to filter out visual distractors when tracking objects with generic models based on shape representations. Experimental results compared against the CONDENSATION algorithm have demonstrated superior tracking performance.
Topic models for scene analysis and abnormality detection

Automatic analysis and understanding of common activities and detection of deviant behaviors is a challenging task in computer vision. This is particularly true in surveillance data, where busy traffic scenes are rich with multifarious activities many of them occurring simultaneously. In this paper, we address these issues with an unsupervised learning approach relying on probabilistic Latent Semantic Analysis (pLSA) applied to a rich set visual features including motion and size activities for discovering relevant activity patterns occurring in such scenes. We then show how the discovered patterns can directly be used to segment the scene into regions with clear semantic activity content. Furthermore, we introduce novel abnormality detection measures within the scope of the adopted modeling approach, and investigate in detail their performance with respect to various issues. Experiments on 45 minutes of video captured from a busy traffic scene and involving abnormal events are conducted.
Exploiting long-term observations for track creation and deletion in online multi-face tracking

In many visual multi-object tracking applications, the question when to add or remove a target is not trivial due to, for example, erroneous outputs of object detectors or observation models that cannot describe the full variability of the objects to track. In this paper, we present a real-time, online multi-face tracking algorithm that effectively deals with missing or uncertain detections in a principled way. The tracking is formulated in a multi-object state-space Bayesian filtering framework solved with Markov Chain Monte Carlo. Within this framework, an explicit probabilistic filtering step relying on head detections, likelihood models, and long term observations as well as object track characteristics has been designed to take the decision on when to add or remove a target from the tracker. The proposed method applied on three challenging datasets of more than 9 hours shows a significant performance increase compared to a traditional approach relying on head detection and likelihood models only.
Detecting queues at vending machines: A statistical layered approach

This paper presents a method for monitoring activities at a ticket vending machine in a video-surveillance context. Rather than relying on the output of a tracking module, which is prone to errors, the events are directly recognized from image measurements. This especially does not require tracking. A statistical layered approach is proposed, where in the first layer, several sub-events are defined and detected using a discriminative approach. The second layer uses the result of the first and models the temporal relationships of the high-level event using a hidden Markov model (HMM). Results are assessed on 3h30 hours of real video footage coming from Turin metro station.
Determination of singular points in 2D deformable flow fields

Digital image analysis appears to be more and more relevant to the study of physical phenomena involving fluid motion, and of their evolution over time. In that context, 2D deformable motion analysis is one of the important issues to be investigated. The interpretation of such deformable 2D flow fields can generally be stated as the characterization of linear models provided that first order approximations are considered in an adequate neighborhood of so-called singular points, where the velocity becomes null. This paper describes an efficient method, based on a statistical approach, which explicitly addresses these problems, and allows us to locate, characterize and track such singular points in an image sequence. It does not require the prior computation of the velocity field. The method has been validated by experiments carried out with synthetic and real examples corresponding to meteorological image sequences. In fact, the described approach can be of interest in different applications dealing with the characterization of vector fields.
Multi-camera open space human activity discovery for anomaly detection

We address the discovery of typical activities in video stream contents and its exploitation for estimating the abnormality levels of these streams. Such estimates can be used to select the most interesting cameras to show to a human operator. Our contributions come from the following facets: i) the method is fully unsupervised and learns the activities from long term data; ii) the method is scalable and can efficiently handle the information provided by multiple un-calibrated cameras, jointly learning activities shared by them if it happens to be the case (e.g. when they have overlapping fields of view); iii) unlike previous methods which were mainly applied to structured urban traffic scenes, we show that ours performs well on videos from a metro environment where human activities are only loosely constrained.
A conditional random field approach for face identification in broadcast news using overlaid text

We investigate the problem of face identification in broadcast programs where people names are obtained from text overlays automatically processed with Optical Character Recognition (OCR) and further linked to the faces throughout the video. To solve the face-name association and propagation, we propose a novel approach that combines the positive effects of two Conditional Random Field (CRF) models: a CRF for person diarization (joint temporal segmentation and association of voices and faces) that benefit from the combination of multiple cues including as main contributions the use of identification sources (OCR appearances) and recurrent local face visual background (LFB) playing the role of a namedness feature; a second CRF for the joint identification of the person clusters that improves identification performance thanks to the use of further diarization statistics. Experiments conducted on a recent and substantial public dataset of 7 different shows demonstrate the interest and complementarity of the different modeling steps and information sources, leading to state of the art results.
Audio-visual speaker tracking with importance particle filters

We present a probabilistic method for audio-visual (AV) speaker tracking, using an uncalibrated wide-angle camera and a micro- phone array. The algorithm fuses 2-D object shape and audio information via importance particle filters (I-PFs), allowing for the asymmetrical integration of AV information in a way that efficiently exploits the complementary features of each modality. Audio localization information is used to generate an importance sampling (IS) function, which guides the random search process of a particle filter towards regions of the configuration space likely to contain the true configuration (a speaker). The measurement process integrates contour-based and audio observations, which results in reliable head tracking in realistic scenarios. We show that imperfect single modalities can be combined into an algorithm that automatically initializes and tracks a speaker, switches between multiple speakers, tolerates visual clutter, and recovers from total AV object occlusion, in the context of a multimodal meeting room.
Detection-based multi-human tracking using a CRF model

In surveillance videos, the task of tracking multiple people is of primary importance and is often a preliminary step before applying higher-level algorithms, e.g. to analyze interactions or to recognize behaviors. In this paper, we take a tracking-by-detection approach and formulate multi-person tracking as a statistical data association problem which seeks for the optimal label field in which detections belonging to the same person have the same label. Specifically, unlike most previous works that rely on generative approaches, we use a Conditional Random Field (CRF) model, whose pairwise detection factors, defined for both distance and color features, are modeled using a two-hypothesis framework: a pair of detections corresponds either to the same person or not. Parameters of these two-hypothesis model factors are learned in a fully unsupervised way from data. Optimization is conducted using a deterministic sliding window method. Qualitative and quantitative results on several different surveillance datasets show that our method can generate robust and accurate tracks in spite of the noisy output of the human detector and of occlusions.
Application of Information Retrieval Technologies to Presentation Slides

Presentations are becoming an increasingly more common means of communication in working environments, and slides are often the necessary supporting material on which the presentations rely. In this paper, we describe a slide indexing and retrieval system in which the slides are captured as images (through a framegrabber) at the moment they are displayed during a presentation and then transcribed with an optical character recognition (OCR) system. In this context, we show that such an approach presents several advantages over the use of commercial software (API based) to obtain the slide transcriptions. We report a set of retrieval experiments conducted on a database of 26 real presentations (570 slides) collected at a workshop. The experiments show that the overall retrieval performance is close to that obtained using either a manual transcription of the slides or the API software. Moreover, the experiments show that the OCR-based approach outperforms significantly the API in extracting the text embedded in images and figures
Time-sensitive topic models for action recognition in videos

In this paper, we postulate that temporal information is important for action recognition in videos. Keeping temporal information, videos are represented as word×time documents. We propose to use time-sensitive probabilistic topic models and we extend them for the context of supervised learning. Our time-sensitive approach is compared to both PLSA and Bag-of-Words. Our approach is shown to both capture semantics from data and yield classification performance comparable to other methods, outperforming them when the amount of training data is low.
Robust and Accurate 3D Head Pose Estimation through 3DMM and Online Head Model Reconstruction

Accurate and robust 3D head pose estimation is important for face related analysis. Though high accuracy has been achieved by previous works based on 3D morphable model (3DMM), their performance drops with extreme head poses because such models usually only represent the frontal face region. In this paper, we present a robust head pose estimation framework by complementing a 3DMM model with an online 3D reconstruction of the full head providing more support when handling extreme head poses. The approach includes a robust online 3DMM fitting step based on multi-view observation samples as well as smooth and face-neutral synthetic samples generated from the reconstructed 3D head model. Experiments show that our framework achieves state-of-the-art pose estimation accuracy on the BIWI dataset, and has robust performance for extreme head poses when tested on natural interaction sequences.
Localized anomaly detection via hierarchical integrated activity discovery

With the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this article, we consider recent methods based on temporal probabilistic models and improve them in multiple ways. Our contributions are the following: (i) we integrate the low level processing and the temporal activity modeling, showing how this feedback improves the overall quality of the captured information, (ii) we show how the same approach can be taken to do hierarchical multi-camera processing, (iii) we use spatial analysis of the anomalies both to perform local anomaly detection and to frame automatically the detected anomalies. We illustrate the approach on both traffic data and videos coming from a metro station.
Evaluation of Multiple Cue Head Pose Estimation Algorithms in Natural Environements

Head pose estimation is a research area which has many applications, e.g. in human computer interfaces design or in the analysis of people's focus-of-attention. The paper addresses the issue of head pose estimation, and makes two contributions. First it introduces a database of more than 2 hours of video with head pose annotation involving people engaged in office activities or meeting discussion. The database is publicly available. The second is an algorithm which couples tracking and head pose estimation in a mixed-state particle filter. The approach combines the robustness of color-based tracking by exploiting skin head/face models with the localization accuracy of texture-based head models, as demonstrated by the reported experiments
HeadFusion: 360° Head Pose Tracking Combining 3D Morphable Model and 3D Reconstruction

Head pose estimation is a fundamental task for face and social related research. Although 3D morphable model (3DMM) based methods relying on depth information usually achieve accurate results, they usually require frontal or mid-profile poses which preclude a large set of applications where such conditions can not be garanteed, like monitoring natural interactions from fixed sensors placed in the environment. A major reason is that 3DMM models usually only cover the face region. In this paper, we present a framework which combines the strengths of a 3DMM model fitted online with a prior-free reconstruction of a 3D full head model providing support for pose estimation from any viewpoint. In addition, we also proposes a symmetry regularizer for accurate 3DMM fitting under partial observations, and exploit visual tracking to address natural head dynamics with fast accelerations. Extensive experiments show that our method achieves state-of-the-art performance on the public BIWI dataset, as well as accurate and robust results on UbiPose, an annotated dataset of natural interactions that we make public and where adverse poses, occlusions or fast motions regularly occur.
A conditional random field approach for audio-visual people diarization

We investigate the problem of audio-visual (AV) person diarization in broadcast data. That is, automatically associate the faces and voices of people and determine when they appear or speak in the video. The contributions are twofolds. First, we formulate the problem within a novel CRF framework that simultaneously performs the AV association of voices and face clusters to build AV person models, and the joint segmentation of the audio and visual streams using a set of AV cues and their association strength. Secondly, we use for this AV association strength a score that does not only rely on lips activity, but also on contextual visual information (face size, position, number of detected faces,...) that leads to more reliable association measures. Experiments on 6 hours of broadcast data show that our framework is able to improve the AV-person diarization especially for speaker segments erroneously labeled in the mono-modal case.
Temporally subsampled detection for accurate and efficient face tracking and diarization

Face diarization, i.e. face tracking and clustering within video documents, is useful and important for video indexing and fast browsing but it is also a difficult and time consuming task. In this paper, we address the tracking aspect and propose a novel algorithm with two main contributions. First, we propose an approach that leverages state-of-the-art deformable part-based model (DPM) face detector with a multi-cue discriminant tracking-by-detection framework that relies on automatically learned long-term time-interval sensitive association costs specific to each document type. Secondly to improve performance, we propose an explicit false alarm removal step at the track level to efficiently filter out wrong detections (and resulting tracks). Altogether, the method is able to skip frames, i.e. process only 3 to 4 frames per second - thus cutting down computational cost - while performing better than state-of-the-art methods as evaluated on three public benchmarks from different context including a movie and broadcast data.
Learning large margin likelihoods for realtime head pose tracking

We consider the problem of head tracking and pose estimation in realtime from low resolution images. Tracking and pose recognition are treated as two coupled problems in a probabilistic framework: a template-based algorithm with multiple pose-specific reference models is used to determine jointly the position and the scale of the target and its head pose. Target representation is based on Histograms of Oriented Gradients (HOG): descriptors which are at the same time robust under varying illumination, fast to compute and discriminative with respect to pose. To improve pose recognition accuracy, we define the likelihood as a parameterized function and we propose to learn it from training data with a new discriminative approach based on the large-margin paradigm. The performance of the learning algorithm and the tracking are evaluated on public images and video databases.