nils-herrmann/clean_scirepeval huggingface dataset:
More robust face recognition by considering occlusion information

This paper addresses one of the main challenges of face recognition (FR): facial occlusions. Currently, the human brain is the most robust known FR approach towards partially occluded faces. Nevertheless, it is still not clear if humans recognize faces using a holistic or a component-based strategy, or even a combination of both. In this paper, three different approaches based on principal component analysis (PCA) are analyzed. The first one, a holistic approach, is the well-known eigenface approach. The second one, a component-based method, is a variation of the eigenfeatures approach, and finally, the third one, a near-holistic method, is an extension of the lophoscopic principal component analysis (LPCA). So the main contributions of this paper are: The three different strategies are compared and analyzed for identifying partially occluded faces and furthermore it explores how a priori knowledge about present occlusions can be used to improve the recognition performance.
Video representation and coding using a sparse steered mixture-of-experts network

In this paper, we introduce a novel approach for video compression that explores spatial as well as temporal redundancies over sequences of many frames in a unified framework. Our approach supports “compressed domain vision” capabilities. To this end, we developed a sparse Steered Mixture-of-Experts (SMoE) regression network for coding video in the pixel domain. This approach drastically departs from the established DPCM/Transform coding philosophy. Each kernel in the Mixture-of-Experts network steers along the direction of highest correlation, both in spatial and temporal domain, with local and global support. Our coding and modeling philosophy is embedded in a Bayesian framework and shows strong resemblance to Mixture-of-Experts neural networks. Initial experiments show that at very low bit rates the SMoE approach can provide competitive performance to H.264.
Theoretical consideration of global motion temporal filtering

A widely used technique to reduce the noise variance of a signal is a temporal overlapping of several noisy versions of it. It will be shown that the same idea can be applied for video sequences. Several versions of the current frame can be aligned using motion compensation that adjacent frames represent a noisy version of the current frame. In a first theoretical calculation of this concept combining the temporal overlapping of several noisy versions of the same signal and a rate-distortion equation, it has been shown that a theoretical bit rate reduction of 1/2 log2(N) is possible. In this work, the concept will be advanced to be closer to practice by adding a model for the motion estimation error. It will be shown that the derived theoretical equation confirms the practice and models the behavior of a video encoding environment using parametric motion compensated temporal filtering very well.
Optimal block-overlapping synthesis transforms for coding images and video at very low bitrates

In this paper we address the problem of coding images and video at very low bitrates using conventional hybrid differential pulse code modulation/discrete cosine transform (DPCM/DCT) coding schemes with reduced blocking artifacts. To this end, optimal block-overlapping synthesis filterbanks (block-overlapping inverse transform kernels) with minimum least-squares reconstruction error properties are derived which replace the conventional inverse DCT if only a few DCT coefficients are transmitted to the receiver. Results show that the proposed inverse block-overlapping transforms can significantly outperform the conventional inverse DCT both in terms of objective and subjective quality measures.
Consensus-based multiview texturing and depth-map completion

We describe an active depth imaging system based on phase measuring triangulation. Typically depth-maps generated with such 3D scanning systems suffer from occlusions and imperfections, especially in the vicinity of depth discontinuities. Applying multiple color images, captured with a camera array, for view synthesis from the erroneous depth-maps can result in severe texturing artifacts. Our consensus-based approach greatly reduces these artifacts by comparing the similarity of the multiview texture images during the blending process to detect outliers in the form of foreground texture projected on background surfaces and specular ambiguity. Additionally, the approach is applied to dramatically improve the depth-maps by generating multiple depth-map hypotheses and selecting the areas of each that have the highest consensus with the set of multiview texture images. Our approach yields accurate and occlusion-free depth-maps in real-time.
Document Capturing Method with a Camera Using Robust Feature Points Detection

This paper presents a method of capturing a document with a single camera based on the Hough transform with reduced angle domain. Document image capturing with a camera has some advantages over traditional flatbed scanners. A problem of capturing a document with a camera is to find a paper region in an image and find robust features to remove perspective distortions. If we have boundary information of a paper with a document to extract feature points, a homography could be estimated to remove perspective distortions. In this paper, text regions in a document are removed with edge plane decomposition to detect the region and the boundary of a paper in an image from a camera, because characters in a document make outliers, which make it difficult to find features. After removing text regions in a document, feature points to remove perspective distortions are extracted by the Hough transform in reduced angle domain from each decomposed edge plane. The experimental results show that the proposed method successfully removes text regions, which are outliers of finding line features to eliminate perspective distortions. A document region is extracted from the image successfully.
Assessing post-detection filters for a generic pedestrian detector in a tracking-by-detection scheme

Tracking-by-detection becomes more and more popular for visual pedestrian tracking applications. However, it requires accurate and reliable detections in order to obtain good results. In this work, we propose two different post-detection filters designed to enhance the performance of custom person detectors. Using a popular deformable-parts-based pedestrian detector as a baseline, a detailed comparison over multiple test videos is performed and the gain of both algorithms is proven. Further analysis shows that the improved detection outcomes also lead to improved tracking results. We thus found that the usage of the proposed post-detection filters is recommendable as they do not impose a high computational load and are not limited to a specific detector method.
Feasibility of Multi-View Video Streaming Over P2P Networks

We propose to stream multi-view video over a multi-tree peer- to-peer (P2P) network using the NUEPMuT protocol. Each view of the multi-view video is streamed over an independent P2P streaming tree and each peer only contributes upload capacity in a single tree, in order to limit the adverse effects of ungraceful peer departures. Additionally, we investigate the feasibility of using the proposed P2P networking architecture, NUEPMuT, for the streaming of multi-view video content with the currently available Internet connection bandwidths.
Video coding using global motion temporal filtering

Recent deblocking techniques are based on spatial filtering. We present a new deblocking technique based on temporal filtering of spatially aligned frames. This approach is used in an H.264/AVC coding environment. The algorithm estimates the ideal amount of frames used for temporal filtering at the encoder side. In that way it is assured that the receiver is presented with the best possible visual quality in terms of structural similarity. Theoretical consideration of the problem proves the concept of the new approach. Experimental evaluation shows that the new temporal deblocking filter significantly improves visual quality and reduces bit rate compared to common H.264/AVC deblocking by up to 18%.
On the Use of Auditory Representations for Sparsity-Based Sound Source Separation

Sparsity-based source separation algorithms often rely on a transformation into a sparse domain to improve mixture disjointness and therefore facilitate separation. To this end, the most commonly used time-frequency representation has been the short time Fourier transform (STFT). The purpose of this paper is to study the use of auditory-based representations instead of the STFT. We first evaluate the STFT disjointness properties for the case of speech and music signals, and show that auditory representations based on the equal rectangular bandwidth (ERB) and Bark frequency scales can improve the disjointness of the transformed mixtures
Background modeling for video coding: From sprites to Global Motion Temporal filtering

Techniques for modeling the background of a video sequence can be useful in alternative video coding approaches. Sprite coding has been evolved to provide high quality decoded video frames after transmission by a reduced amount of bits. However, it has also been shown that this works only for a certain kind of video sequences. It also meets quality limits due to the Sprite generation step. To tackle this problem, multiple Sprites have been proposed. Considering the improved quality coming with multiple Sprites, a method has been developed which provides an optimal quality, i.e. local background Sprite generation. This method can be used to conduct Global Motion Temporal Filtering (GMTF) of distorted video frames. In a first application, GMTF is applied as a post-processing deblocking filter in a video coding environment, which is experimentally evaluated.
Adaptive And Quasi-physiological Controller For The Total Artificial Heart

An adaptive and quasi-physiological controller for the total artificial heart (TAH) has been designed on the basis of a computer model of the cardiovascular system. In correspondence with the patient's metabolic demand under various conditions the controller uses the Starling-mechanism of the human body and the measured right atrial pressure to control the heart frequency. Several time-varying physiological parameters of the patient, as e.g. the peripheral resistance to flow, can be used to adapt the dynamic performance of the controller with the help of parameter estiniation techniques. The controller has been tested in several computer experiments, and reliable results were obtained including stability and physiological adaptation of the controller under a wide range of environmental disturbances and exercises carried out by the patient.
Adaptive Image Warping for Hole Prevention in 3D View Synthesis

Increasing popularity of 3D videos calls for new methods to ease the conversion process of existing monocular video to stereoscopic or multi-view video. A popular way to convert video is given by depth image-based rendering methods, in which a depth map that is associated with an image frame is used to generate a virtual view. Because of the lack of knowledge about the 3D structure of a scene and its corresponding texture, the conversion of 2D video, inevitably, however, leads to holes in the resulting 3D image as a result of newly-exposed areas. The conversion process can be altered such that no holes become visible in the resulting 3D view by superimposing a regular grid over the depth map and deforming it. In this paper, an adaptive image warping approach as an improvement to the regular approach is proposed. The new algorithm exploits the smoothness of a typical depth map to reduce the complexity of the underlying optimization problem that is necessary to find the deformation, which is required to prevent holes. This is achieved by splitting a depth map into blocks of homogeneous depth using quadtrees and running the optimization on the resulting adaptive grid. The results show that this approach leads to a considerable reduction of the computational complexity while maintaining the visual quality of the synthesized views.
Recent developments in panoramic image generation and sprite coding

The composition of panoramic images has recently received considerable attention. While panoramic images were first used mainly as a flexible visualization technique, they also found application in video coding, video enhancement, format conversion, and content analysis. The topic has enlarged and diverged into many specialized research directions, which makes it difficult to stay in touch with recent developments. This paper intends to give an overview of the current state of research, including recent developments. Two of the applications of sprite coding and global-motion estimation are presented in more detail to provide some insights into the system aspects.
Super-resolution Mosaicing using Embedded Hybrid Recursive Folow-based Segmentation

We present a new strategy for the generation of background super-resolution mosaics from videos with arbitrary camera pan, tilt, and zoom including freely moving foreground. Our main focus is directed to the automatic, embedded pre-segmentation of foreground objects. The segmentation technique is based on efficient and robust computation of the optical flow between neighboring frames in a video scene using a hybrid recursive approach, i.e. a combination of block-flow methods and spatial-temporal anisotropic diffusion-based flow field regularization. Unlike in other related publications we are able to segment moving foreground objects before the actual image-to-mosaic-registration is proceeded even if the foreground objects do not move relatively to the camera motion. Thus, every segmented background frame can be used to enhance the resolution of the composed mosaic due to an effective blending process. Additionally, the appearance of disturbing ghost objects is prevented
Resampling of multiple camera point cloud data

Even though the sampling grid of a digital camera is uniform and rectangular, the depth information reconstructed from multi-camera samples is neither uniform nor rectangular due to the rotations and translations of the camera coordinate systems with respect to each other. In order to facilitate further processing, e.g. compression of multi camera depth data, resampling of non-uniform multi-camera depth samples to a uniform rectangular grid is advantageous. In this paper, we introduce and compare two resampling methods using a voxel-based and a triangular-mesh-based approach.
A consistent two-level metric for evaluation of automated abandoned object detection methods

Scientific interest in automated abandoned object detection algorithms using visual information is high and many related systems have been published in recent years. However, most evaluation techniques rely only on statistical evaluation on the object level. Therefore and due to benchmarks with commonly only few abandoned objects and a non-standardized evaluation procedure, an objective performance comparison between different methods is generally hard. We propose a new evaluation metric which is focused on an end-user application case and an evaluation protocol which eliminates uncertainties in previous performance assessments. Using two variants of an abandoned object detection method, we show the features of the novel metric on multiple datasets proving its advantages over previously used measures.
Adaptive Temporal Trajectory Filtering for Video Compression

Most in-loop filters currently being employed in video compression algorithms use spatial information from a single frame of the video sequence only. In this paper, a new filter is introduced and investigated that combines both spatial and temporal information to provide subjective and objective quality improvement. The filter only requires a small overhead on slice level while using the temporal information conveyed in the bit stream to reconstruct the individual motion trajectory of every pixel in a frame at both encoder and decoder. This information is then used to perform pixel-wise adaptive motion-compensated temporal filtering. It is shown that the filter performs better than the state-of-the-art codec H.264/AVC over a large range of sequences and bit rates. Additionally, the filter is compared with another, Wiener-based in-loop filtering approach and a complexity analysis of both algorithms is conducted.
Camera motion-constraint video codec selection

In recent years advanced video codecs have been developed, such as standardized in MPEG-4. The latest video codec H.264/AVC provides compression performance superior to previous standards, but is based on the same basic motion-compensated-DCT architecture. However, for certain types of video, it has been shown that it is possible to outperform the H.264/AVC using an object-based video codec. Towards a general-purpose object-based video coding system we present an automated approach to separate a video sequences into sub-sequences regarding its camera motion type. Then, the sub-sequences are coded either with an object-based codec or the common H.264/AVC. Applying different video codecs for different kinds of camera motion, we achieve a higher overall coding gain for the video sequence. In first experimental evaluations, we demonstrate the excellence performance of this approach on two test sequences.
Efficient real-time local optical flow estimation by means of integral projections

In this paper we present an approach for the efficient computation of optical flow fields in real-time and provide implementation details. Proposing a modification of the popular Lucas-Kanade energy functional based on integral projections allows us to speed up the method notably. We show the potential of this method which can compute dense flow fields of 640×480 pixels at a speed of 4 fps in a GPU implementation based on the OpenCL framework. Working on sparse optical flow fields of up to 17,000 points, we reach execution times of 70 fps. Optical flow methods are used in many different areas, the proposed method speeds up current surveillance algorithms used for scene description and crowd analysis or Augmented Reality and robot navigation applications.
Video-genre-classification: recognizing cartoons in real-time using visual-descriptors and a multilayer-percetpron

We present a new approach for classifying MPEG-video sequences as `cartoon' or `noncartoon' by analyzing specific color, texture and motion features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 [T. Sikora et al. (2002)]. In our method the extracted features from the visual descriptors are nonlinear weighted with a sigmoid-function and afterwards combined using a multilayered perceptron to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 100 representative video sequences (20 cartoons and 4*20 noncartoons) gathered from free digital TV-broadcasting
Global motion temporal filtering for in-loop deblocking

One of the most severe problems in hybrid video coding is its block-based approach, which leads to distortions called blocking artifacts. These artifacts affect not only the subjective perception at the receiver but also the motion compensated prediction (MCP) that generates a prediction signal from previously decoded pictures. It is therefore directly connected to the amount of data that has to be transmitted. In this paper, we propose a technique called global motion temporal filtering for blocking artifact reduction. Other than common deblocking techniques, this approach does not reduce the blocking artifacts spatially. Filtering is performed temporally using a set of neighboring pictures from the picture buffer. This approach is incorporated into an H.264/AVC reference software. Experimental evaluation shows that the proposed technique significantly improves the quality in terms of rate-distortion performance.
Robust Local Optical Flow for Feature Tracking

This paper is motivated by the problem of local motion estimation via robust regression with linear models. In order to increase the robustness of the motion estimates, we propose a novel robust local optical flow approach based on a modified Hampel estimator. We show the deficiencies of the least squares estimator used by the standard Kanade-Lucas-Tomasi (KLT) tracker when the assumptions made by Lucas-Kanade are violated. We propose a strategy to adapt the window sizes to cope with the generalized aperture problem. Finally, we evaluate our method on the Middlebury and MIT dataset and show that the algorithm provides excellent feature tracking performance with only slightly increased computational complexity compared to KLT. To facilitate further development, the presented algorithm can be downloaded from http://www.nue.tu-berlin.de/menue/forschung/projekte/rlof.
UA-DETRAC 2017: Report of AVSS2017 & IWT4S Challenge on Advanced Traffic Monitoring

The rapid advances of transportation infrastructure have led to a dramatic increase in the demand for smart systems capable of monitoring traffic and street safety. Fundamental to these applications are a community-based evaluation platform and benchmark for object detection and multi-object tracking. To this end, we organize the AVSS2017 Challenge on Advanced Traffic Monitoring, in conjunction with the International Workshop on Traffic and Street Surveillance for Safety and Security (IWT4S), to evaluate the state-of-the-art object detection and multi-object tracking algorithms in the relevance of traffic surveillance. Submitted algorithms are evaluated using the large-scale UA-DETRAC benchmark and evaluation protocol. The benchmark, the evaluation toolkit and the algorithm performance are publicly available from the website http://detrac-db.rit.albany.edu.
Clustering Motion for Real-Time Optical Flow Based Tracking

The selection of regions or sets of points to track is a key task in motion-based video analysis, which has significant performance effects in terms of accuracy and computational efficiency. Computational efficiency is an unavoidable requirement in video surveillance applications. Well established methods, e.g. Good Features to Track, select points to be tracked based on appearance features such as cornerness and therefore neglecting the motion exhibited by the selected points. In this paper, we propose an interest point selection method that takes into account the motion of previously tracked points in order to constrain the number of point trajectories needed. By defining pair-wise temporal affinities between trajectories and representing them in a minimum spanning tree, we achieve a very efficient clustering. The number of trajectories assigned to each motion cluster is adapted by initializing and removing tracked points by means of feed-back. Compared to the KLT tracker, we save up to 65% of the points to track, therefore gaining in efficiency while not scarifying accuracy.
Components and Their Topology for Robust Face Detection in the Presence of Partial Occlusions

This paper presents a novel approach for automatic and robust object detection. It utilizes a component-based approach that combines techniques from both statistical and structural pattern recognition domain. While the component detection relies on Haar-like features and an AdaBoost trained classifier cascade, the topology verification is based on graph matching techniques. The system was applied to face detection and the experiments show its outstanding performance in comparison to conventional face detection approaches. Especially in the presence of partial occlusions, uneven illumination, and out-of-plane rotations, it yields higher robustness. Furthermore, this paper provides a comprehensive review of recent approaches for object detection and gives an overview of available databases for face detection.