nils-herrmann/clean_scirepeval huggingface dataset:
A system architecture for IP-camera based driver assistance applications

We present a novel system architecture for automotive IP-camera based driver assistance applications. We assume that the video data is carried by an in-car switched IP/Ethernet network. The proposed architecture borrows concepts from the CE industry and hence fully takes advantage of the corresponding price and performance advantages. As an implementation study, a top view service is investigated. The suitability of the proposed approach is evaluated by introducing performance measures reflecting the special requirements of the driver assistance domain. To meet the real-time requirements of the driver assistance services we investigate and compare different architectural approaches for IP network cameras. Based on the results obtained with the prototypical implementations, an appropriate solution for future IP-camera based driver assistance services is proposed.
An experimental study of lossy compression in a real telepresence and teleaction system

High network requirements of multiple degrees of freedom haptic data exchange represent a challenge in modern telepresence and teleaction systems. This study presents a systematic evaluation of a psychophysics-based approach to lossy haptic data compression proposed in the literature: the deadband approach and its extension which introduces additional signal model based prediction. In an experimental study, the effects of these approaches on packet rate reduction, perceived interface quality, and relevant task performance criteria were investigated in a three degrees of freedom telepresence and teleaction system. The results indicate that the extended approach did not lead to significant data reduction and adversely affected perceived interface quality as well as task performance. Without prediction, the deadband approach showed excellent rate reduction performance without adversely affecting perceived interface quality and most task performance criteria. We conclude from our study that the combined deadband and prediction approach is not practical for a telepresence and teleaction system with the used control structure, while the deadband compression approach alone exceeded expectations.
RD-Optimized Rate Shaping for Multiple Scalable Video Streams

Delivering streaming video over wired and wireless networks poses many challenges, primarily due to the throughput variations caused by time-varying network conditions. Scalable video coding gives an elegant way to adapt the video streams to the available transmission resource. In this paper, we consider a multi-user scenario wherein multiple scalable video streams compete for a shared transmission link with limited forwarding capacity. We propose a rate-distortion (RD) optimized rate shaping approach to improve the overall video quality. For this, compact RD side information is sent along with the sequences. Our simulation results show that significant improvements are achieved by the proposed RD-optimized rate shaping approach compared to conventional priority-based rate shaping.
Rate-distortion optimized video streaming with adaptive playout

We propose a new scheme for streaming media systems that combines adaptive media playout (AMP) with rate-distortion (R-D)optimized packet transmission. AMP, the client-controlled, adaptive modification of the media playout rate, allows us to flexibly adjust the playout deadlines of individual packets, and can therefore reduce reconstruction distortion. This added flexibility incurs a subjective cost, however. We introduce functions that assess the subjective cost of a schedule of playout rate modifications, and we show how to optimize the schedule with respect to these costs and distortion. Because the optimal playout rate schedule and the R-D optimal transmission schedule are interdependent, we solve the two problems jointly. In simulations that model a receiver-driven scenario, results for a short media clip show a more than 2 dB improvement in mean PSNR for R-D optimized transmission scheduling combined with a moderate amount of AMP, over R-D optimized transmission scheduling alone.
Compression of Bayer-Pattern Video Sequences Using Adjusted Chroma Subsampling

Most consumer digital color cameras are equipped with a single chip. Such cameras capture only one color component per pixel (e.g., Bayer pattern) instead of an RGB triple. Conventionally, missing color components at each pixel are interpolated from its neighboring pixels, so that full color images are constructed. This process is typically referred to as demosaicing. After demosaicing, the full resolution RGB video frames are converted into YUV color space. U and V are then typically subsampled by a factor of four and the resulting video data in the 4:2:0 format become the input for the video encoder. In this letter, we look into the weakness of the conventional scheme and propose a novel solution for compressing Bayer-pattern video data. The novelty of our work lies largely in the chroma subsampling. We properly choose the locations to calculate the chroma pixels U and V according to the positions of B and R pixels in the Bayer pattern and this leads to higher quality of the reconstructed images. In our experiments, we have observed an improvement in composite peak-signal-to-noise ratio performance of up to 1.5 dB at the same encoding rate. Based on this highly efficient approach, we propose also a low-complexity method which saves almost half of the computation at the expense of a small loss in coding efficiency.
A novel, psychophysically motivated transmission approach for haptic data streams in telepresence and teleaction systems

One of the key challenges in telepresence and teleaction systems is the fact that a global control loop is closed over a communication network. The transmission delay of haptic information is extremely critical. Therefore, new data samples from the haptic sensors are typically immediately forwarded to the receiver which leads to a large number of packets being generated when using the Internet as the communication infrastructure. We present a novel approach to reduce the number of packets and, therefore, the amount data communicated in a telepresence and teleaction system. Our method uses a passive deadband transmission approach which only delivers data packets over the network when the sampled sensor data changes more than a given threshold value. The threshold value is determined by psychophysical experiments. This approach leads to a considerable reduction (up to 90%) of packet rate and data rate without sacrificing the fidelity and immersiveness of the system.
Delay compensation in Shared Haptic Virtual Environments

Shared Haptic Virtual Environments (SHVEs) are often realized using a client-server architecture. At the server, a physics simulation engine calculates the object states based on the position information received from the clients. At the clients, the object state information, received from the server, is used to update the local copy of the virtual environment. Forces displayed to the user through a haptic device are computed locally at the client based on the interactions with the objects. Communication delay leads to delayed object state updates and increased interaction forces rendered at the clients. Users perceive this as increased object weight. In this paper, we systematically analyze the loss of transparency caused by communication delay and propose a novel adaptive force rendering scheme to compensate for it. The proposed scheme reduces the objects' stiffness at the clients, based on delay, device velocity and motion constraints of the objects, only if necessary to achieve perceptual transparency. Simulations and subjective evaluations show that the effect of increased weight of objects is successfully compensated for the tested delay range of up to 150 ms. At the same time, if the object is unmovable, the perception of interaction with a rigid object is preserved.
Performance comparison of various feature detector-descriptor combinations for content-based image retrieval with JPEG-encoded query images

We study the impact of JPEG compression on the performance of an image retrieval system for different feature detector-descriptor combinations. The VLBenchmarks retrieval framework is used to compare a total of 60 detector-descriptor combinations for a dataset with JPEG-encoded query images. Our results show that among all tested detectors, the Hessian-Affine detector leads to the most robust performance in the presence of strong JPEG compression. Additionally, we compare the retrieval gains of the different detector-descriptor pairs after processing the JPEG-encoded query images with different deblocking filters. The results illustrate that for the MSER, MFD and WαSH detectors, the retrieval results benefit from two of the deblocking approaches at low bit rate irrespective of what descriptor the detectors are combined with. The same two deblocking filters are found to increase the retrieval performance for the MROGH descriptor when combined with most of the tested detectors.
On the Transparency of Client/Server-Based Haptic Interaction with Deformable Objects

This paper studies the transparency of client/server-based haptic interaction with simulated deformable objects. In the considered remote interaction scenario, the server simulates the computationally expensive finite-element-based object deformation at a low temporal update rate and transmits the result to the clients. There, the received deformation data is applied to the polygon mesh, which is used to locally render force feedback with a penalty-based force rendering algorithm at the required high rate. Based on a one-dimensional deformable object example, we analyze the transparency of this multi-rate architecture for a two-user interaction. Communication delay leads to increased force magnitudes and an increased impedance displayed to the clients that actively interact with the object. We propose a method that adjusts the stiffness used in the local force rendering at the clients to compensate for this effect. The conducted objective and subjective evaluations show that the proposed method successfully compensates for the effect of communication delay in the tested delay range of up to 100 ms.
A performance comparison of multiple description video streaming in peer-to-peer and content delivery networks

We examine the performance of peer-to-peer (P2P) media streaming using multiple description coding (MDC) and compare it with that of a content delivery network (CDN). In both approaches, multiple servers simultaneously serve one requesting client with complementary descriptions. This approach improves reliability and decreases the data rate a server has to provide. We have implemented both approaches in the ns-2 network simulator. The experimental results indicate that the user perceived video quality of MDC-based streaming in a P2P network can be significantly better than in a CDN, despite the high degree of unreliability of the P2P network.
Exploiting prior knowledge in mobile visual location recognition

Mobile visual location recognition needs to be performed in real-time for location based services to be perceived as useful. We describe and validate an approach that eliminates the network delay by preloading partial visual vocabularies to the mobile device. Retrieval performance is significantly increased by composing partial vocabularies based on the uncertainty about the location of the client. This way, prior knowledge is efficiently integrated into the matching process. Based on compressed feature sets, infrequently uploaded from the mobile device, the server estimates the client location and its uncertainty by fusing consecutive query results using a particle filter.
On the compression and rendering of event-triggered force transients in networked virtual environments

When haptically interacting with virtual rigid bodies, the rendering and display of realistic haptic force-feedback is a challenging task. Conventionally rendered closed-loop proportional feedback forces according to Hooke's law or potential fields impart a feeling of soft contacts and hence are not sufficient to convey the material contact properties of a rigid object. Improvement of haptic contact rendering by incorporating high-frequency transients immediately after contact leads to high packet rates in networked virtual environments (VEs). In this work, we combine event-triggered contact force transients with perceptual coding of haptic signals to achieve a two-fold objective. A significant reduction in data and packet-rates on the network is obtained while improving the realism of interaction with rigid objects, verified by our psychophysical experiments. Furthermore, we describe our observation that the introduction of contact transients serves to mask the artifacts of the perceptual coding scheme to some extent. This allows us to push the data reduction performance beyond what is normally achievable by the haptic compression scheme alone.
Proxy-Based Reference Picture Selection for Error Resilient Conversational Video in Mobile Networks

We propose a frame dependency management strategy for error robust transmission of conversational video in mobile networks. We consider an end-to-end video transmission scenario that involves both a wireless uplink as well as a wireless downlink plus some intermediate wireline network transmission. We also investigate the special cases of an end-to-end scenario where only a wireless uplink or a wireless downlink is present. We cope with packet loss on the downlink by retransmitting lost packets from the base station to the receiver for error recovery. Retransmissions are enabled by using fixed-distance reference picture selection during encoding with a prediction distance that corresponds to the round-trip time of the downlink combined with accelerated decoding. We deal with transmission errors on the uplink by sending acknowledgments and predicting the next frame to encode from those slices that have been correctly received by the base station. We show that these two separate approaches for uplink and downlink efficiently complement one another and the resulting end-to-end scheme is characterized by very low computational complexity. We compare our scheme to several state-of-the-art error resiliency approaches and report significant improvements.
On the Minimization of Glass-to-Glass and Glass-to-Algorithm Delay in Video Communication

Video cameras are increasingly used to provide real-time feedback in automatic control systems, such as autonomous driving and robotics systems. For such highly dynamic applications, the glass-to-glass (G2G) and glass-to-algorithm (G2A) latencies are critical. In this paper, we analyze the latencies in a point-to-point video transmission system and propose novel frame skipping and preemption approaches to reduce the G2G and G2A delays. We implement the proposed approaches in a prototype that shows significantly reduced G2G and G2A latencies as well as reduced transmission bitrate requirements compared with traditional video transmission schemes. In our low-delay video communication prototype, a VGA resolution video is transmitted with average G2G and G2A delays of 21.2 and 11.5 ms, respectively, with off-the-shelf hardware.
TUMindoor: An extensive image and point cloud dataset for visual indoor localization and mapping

Recent advances in the field of content-based image retrieval (CBIR) have made it possible to quickly search large image databases using photographs or video sequences as a query. With appropriately tagged images of places, this technique can be applied to the problem of visual location recognition. While this task has attracted large interest in the community, most existing approaches focus on outdoor environments only. This is mainly due to the fact that the generation of an indoor dataset is elaborate and complex. In order to allow researchers to advance their approaches towards the challenging field of CBIR-based indoor localization and to facilitate an objective comparison of different algorithms, we provide an extensive, high resolution indoor dataset. The free for use dataset includes realistic query sequences with ground truth as well as point cloud data, enabling a localization system to perform 6-DOF pose estimation.
On the discrimination of stiffness during pressing and pinching of virtual springs

In this paper, we study the perception of stiffness while pressing and pinching virtual springs. These exploration events are highly relevant for telerobotics, but their influence on stiffness perception is largely unexplored. Our study contributes to the understanding of the limits of stiffness perception for human-robot interactions. According to our experiments, mean Weber fraction values between 0.134 and 0.166 are obtained for a reference stiffness value of 200 N/m. Our results also show that the differential sensitivity for stiffness is not significantly affected by the actual exploration event. To test the constancy of the extracted Weber fraction values five further reference stiffness values are evaluated suggesting a constant stiffness sensitivity value in the range of stiffness between 135 and 390 N/m.
Surface classification using acceleration signals recorded during human freehand movement

When a tool is used to tap onto an object or it is dragged over the object surface, vibrations are induced in the tool that can be captured using acceleration sensors. Based on these signals, this paper presents an approach for tool-mediated surface classification which is robust against varying scan-time parameters. We examine freehand recordings of 69 textures and propose a classification system that uses perception-related features such as hardness, roughness and friction as well as selected features adapted from speech recognition such as modified cepstral coefficients. We focus on mitigating the effect of varying contact force and hand speed conditions on these features as a prerequisite for a robust machine-learning-based approach for surface classification. Our system works without explicit scan force and velocity measurements. Experimental results show that our proposed approach allows for successful classification of surface textures under varying freehand movement conditions. The proposed features lead to a classification accuracy of 95% when combined with a Naive Bayes Classifier.
Toward high-fidelity haptic interaction with virtual materials: A robotic material scanning, modelling, and display system

We present a robotic setup for the acquisition of object surface material properties. Our setup is able to collect selected kinesthetic characteristics, such as the surface structure and weight, as well as tactile properties like the friction coefficient and the fine roughness characteristics of the object surface. Additionally, the setup captures the visual appearances of the object. The recorded multimodal sensor data provide all relevant information required to form a haptic model of a material sample. We then use this representation in a standard haptic rendering framework and display the virtual materials using an augmented Phantom Omni device. We conducted a subjective experiment which shows that its participants perceived and rated the rendered virtual materials as similar to the corresponding real materials in a direct comparison test where the users interact simultaneously with the real and the virtual material samples. An overall user rating of 4.3 out of 5.0 is achieved during the subjective experiment.
Investigating the influence of temporal intensity changes on apparent movement phenomenon

This paper aims at investigating the influence of the temporal intensity changes of two low-resolution vibrotactile actuators on the apparent movement phenomenon. In our work, we exploit two human sensory illusions called funneling illusion and apparent movement phenomenon. By temporarily varying the intensities of two adjacent vibrating actuators located on the dorsal of the human forearm, we obtained the illusion of a continuous movement of one tactile stimulus. In this work, we investigated the quality of the apparent movement according to the intensity change of the vibrating motors in a linear and logarithmic pattern. Psychophysical experiments revealed an interesting relationship between the distance and orientation of the two vibrating actuators with the preferred intensity variation, which are presented in this paper.
Coexistence of Time-Triggered and Event-Triggered Traffic in Switched Full-Duplex Ethernet Networks

In the recent years, the Ethernet technology has grown rapidly, mainly due to its applicability in local area networks. High data rates, low cost, collision reduction with the full-duplex approach and the elimination of chaining limits inherent in hubbed Ethernet networks have made the switched Ethernet a dominant network technology. Although the switch technology has improved significantly, the delays appearing in the switches are still not acceptable for time critical applications. This is specially the case when several cascaded switches are applied. Within the scope of developing a new network architecture for the in-vehicle communication, the time constraints of a switched Ethernet network are addressed in this paper. In order to comply with the delay bounds of time critical applications in the automotive field, a cost-effective approach is proposed and analyzed for several cascaded switches.
Toward Haptic Communications Over the 5G Tactile Internet

Touch is currently seen as the modality that will complement audition and vision as a third media stream over the Internet in a variety of future haptic applications which will allow full immersion and that will, in many ways, impact society. Nevertheless, the high requirements of these applications demand networks which allow ultra-reliable and low-latency communication for the challenging task of applying the required quality of service for maintaining the user’s quality of experience at optimum levels. In this survey, we enlist, discuss, and evaluate methodologies and technologies of the necessary infrastructure for haptic communication. Furthermore, we focus on how the fifth generation of mobile networks will allow haptic applications to take life, in combination with the haptic data communication protocols, bilateral teleoperation control schemes and haptic data processing needed. Finally, we state the lessons learned throughout the surveyed research material along with the future challenges and infer our conclusions.
Activity recognition on handheld devices for pedestrian indoor navigation

We propose an inertial sensor-based approach to activity recognition for pedestrian indoor navigation. In the considered scenario a mobile device is held in a hand in front of the user. The recognized activities are the ones relevant to positioning in multi-floor buildings: walking and going up or down the stairs. To model the time dependency between consecutive activities we employ a Hidden Markov Model (HMM). For efficient quantization of continuous features, we apply a random forest classifier. For verification of the proposed algorithm, we conducted experiments with 12 participants and 4 different mobile devices. In our comparison to state-of-the-art approaches, we implement and evaluate major classification algorithms, such as nearest neighbour, decision tree and dynamic Bayesian Network. In the experiments we show the trade-off between computational complexity and classification performance. Furthermore, we demonstrate that the complexity of the HMM can be significantly reduced by replacing it with a dynamic Bayesian network with negligible impact on classification performance. The best of our proposed classifier achieves a classification accuracy of 91% for new users, which offers a 30% improvement compared to state-of-the-art approaches.