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The types of obstacles encountered in the road environment are complex and diverse, and accurate and reliable detection of obstacles is the key to improving traffic safety. Traditional obstacle detection methods are limited by the type of samples and therefore cannot detect others comprehensively. Therefore, this paper proposes an obstacle detection method based on longitudinal active vision. The obstacles are recognized according to the height difference characteristics between the obstacle imaging points and the ground points in the image, and the obstacle detection in the target area is realized without accurately distinguishing the obstacle categories, which reduces the spatial and temporal complexity of the road environment perception. The method of this paper is compared and analyzed with the obstacle detection methods based on VIDAR (vision-IMU based detection and range method), VIDAR + MSER, and YOLOv8s. The experimental results show that the method in this paper has high detection accuracy and verifies the feasibility of obstacle detection in road environments where unknown obstacles exist.

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This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance.

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As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users' previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.

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The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone's 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

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Particle locations determine the whole structure of a granular system, which is crucial to understanding various anomalous behaviors in glasses and amorphous solids. How to accurately determine the coordinates of each particle in such materials within a short time has always been a challenge. In this paper, we use an improved graph convolutional neural network to estimate the particle locations in two-dimensional photoelastic granular materials purely from the knowledge of the distances for each particle, which can be estimated in advance via a distance estimation algorithm. The robustness and effectiveness of our model are verified by testing other granular systems with different disorder degrees, as well as systems with different configurations. In this study, we attempt to provide a new route to the structural information of granular systems irrelevant to dimensionality, compositions, or other material properties.

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The ability to determine the distance to objects is an important feature of most visual systems, but little is known about the neuronal mechanisms for distance estimation. Larval zebrafish execute different visual behaviors depending on distance; at medium distances, they converge their eyes and approach, but when the prey is close enough, they execute a strike and suck the prey into their mouths. To study distance estimation, we developed a head-fixed strike assay. We found that we could evoke strike behavior in head-fixed larvae and quantify head movements to classify the behavior as a strike. Strikes were dependent on distance to prey, allowing us to use them to study distance estimation. Light intensity is rapidly attenuated as it travels through water, so we hypothesized that larvae could use intensity as a distance cue. We found that increasing stimulus intensity could cause larvae to strike at prey that would normally be out of range, and decreasing the intensity could lower the strike rate even for very proximal stimuli. In addition, stimulus contrast is a key parameter, and this could allow larvae to estimate distance over the range of natural illumination. Finally, we presented prey in the binocular vs. monocular visual field and found that monocular prey did evoke strikes, although the binocular input produced more. These results suggest that strike behavior is optimally evoked by bright UV dots in the binocular zone with minimal UV background light and provide a foundation to study the neuronal mechanisms of distance estimation.

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The coronavirus disease (COVID-19) is primarily disseminated through physical contact. As a precaution, it is recommended that indoor spaces have a limited number of people and at least one meter apart. This study proposes a real-time method for monitoring physical distancing compliance in indoor spaces using computer vision and deep learning techniques. The proposed method utilizes YOLO (You Only Look Once), a popular convolutional neural network-based object detection model, pre-trained on the Microsoft COCO (Common Objects in Context) dataset to detect persons and estimate their physical distance in real time. The effectiveness of the proposed method was assessed using metrics including accuracy rate, frame per second (FPS), and mean average precision (mAP). The results show that the YOLO v3 model had the most remarkable accuracy (87.07%) and mAP (89.91%). On the other hand, the highest fps rate of up to 18.71 was achieved by the YOLO v5s model. The results demonstrate the potential of the proposed method for effectively monitoring physical distancing compliance in indoor spaces, providing valuable insights for future use in other public health scenarios.

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Deep learning algorithms have the advantages of a powerful time series prediction ability and the real-time processing of massive samples of big data. Herein, a new roller fault distance estimation method is proposed to address the problems of the simple structure and long conveying distance of belt conveyors. In this method, a diagonal double rectangular microphone array is used as the acquisition device, minimum variance distortionless response (MVDR) and long short-term memory network (LSTM) are used as the processing models, and the roller fault distance data are classified to complete the estimation of the idler fault distance. The experimental results showed that this method could achieve high-accuracy fault distance identification in a noisy environment and had better accuracy than the conventional beamforming algorithm (CBF)-LSTM and functional beamforming algorithm (FBF)-LSTM. In addition, this method could also be applied to other industrial testing fields and has a wide range of application prospects.

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Depth perception as well as egocentric distance estimation can be trained in virtual spaces, although incorrect estimates can occur in these environments. To understand this phenomenon, a virtual environment with 11 changeable factors was created. Egocentric distance estimation skills of 239 participants were assessed with it in the range [25 cm, 160 cm]. One hundred fifty-seven people used a desktop display and seventy-two the Gear VR. According to the results, these investigated factors can have various effects combined with the two display devices on distance estimation and its time. Overall, desktop display users are more likely to accurately estimate or overestimate distances, and significant overestimations occur at 130 and 160 cm. With the Gear VR, distances in the range [40 cm, 130 cm] are significantly underestimated, while at 25 cm, they are significantly overestimated. Estimation times are significantly decreased with the Gear VR. When developing future virtual environments that require depth perception skills, developers should take these results into account.

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Three-dimensional (3D) real-time object detection and tracking is an important task in the case of autonomous vehicles and road and railway smart mobility, in order to allow them to analyze their environment for navigation and obstacle avoidance purposes. In this paper, we improve the efficiency of 3D monocular object detection by using dataset combination and knowledge distillation, and by creating a lightweight model. Firstly, we combine real and synthetic datasets to increase the diversity and richness of the training data. Then, we use knowledge distillation to transfer the knowledge from a large, pre-trained model to a smaller, lightweight model. Finally, we create a lightweight model by selecting the combinations of width, depth & resolution in order to reach a target complexity and computation time. Our experiments showed that using each method improves either the accuracy or the efficiency of our model with no significant drawbacks. Using all these approaches is especially useful for resource-constrained environments, such as self-driving cars and railway systems.

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Despite its mathematical simplicity and ubiquity in imaging technology, there has long been doubt about the ability of linear perspective to best represent human visual space, especially at wide-angle fields of view under natural viewing conditions. We investigated whether changes to image geometry had an impact on participants' performance, specifically in terms of non-metric distance estimates. Our multidisciplinary research team developed a new open-source image database to study distance perception in images by systematically manipulating target distance, field of view, and image projection using non-linear natural perspective projections. The database consists of 12 outdoor scenes of a virtual three-dimensional urban environment in which a target ball is presented at increasing distance, visualised using both linear perspective and natural perspective images, rendered, respectively, with three different fields of view: 100°, 120°, and 140° horizontally. In the first experiment (N = 52), we tested the effects of linear versus natural perspective on non-metric distance judgements. In the second experiment (N = 195), we investigated the influence of contextual and previous familiarity with linear perspective, and individual differences in spatial skills on distance estimations. The results of both experiments showed that distance estimation accuracy improved in natural compared with linear perspective images, particularly at wide-angle fields of view. Moreover, undertaking a training session with only natural perspective images led to more accurate distance judgements overall. We argue that the efficacy of natural perspective may stem from its resemblance to the way objects appear under natural viewing conditions, and that this can provide insights into the phenomenological structure of visual space.

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Due to dataset limitations, existing studies on China's intergenerational income mobility are unreliable. Using longitudinal data from the Chinese Health and Nutrition Survey, this study applied a modified version of the Zimmerman [Zimmerman DJ (1992) Regression toward mediocrity in economic stature. Am Econ Rev 82(3):409-429] model and estimated intergenerational earnings mobility based on a complete model with covariance restrictions. The new estimate demonstrates that intergenerational earnings elasticity in China is 0.54, a rather higher level relative to most developed countries.

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Recently, supplying healthcare services with wearable devices has been investigated. To realize this for true wireless stereo (TWS), which has limited resources (e.g. space, power consumption, and area), implementing multiple functions with one sensor simultaneously is required. The Photoplethysmography (PPG) sensor is a representative healthcare sensor that measures repeated data according to the heart rate. However, since the PPG data are biological, they are influenced by motion artifact and subject characteristics. Hence, noise reduction is needed for PPG data. In this paper, we propose the distance estimation algorithm for PPG signals of TWS. For distance estimation, we designed a waveform adjustment (WA) filter that minimizes noise while maintaining the relationship between before and after data, a lightweight deep learning model called MobileNet, and a PPG monitoring testbed. The number of criteria for distance estimation was set to three. In order to verify the proposed algorithm, we compared several metrics with other filters and AI models. The highest accuracy, precision, recall, and f1 score of the proposed algorithm were 92.5%, 92.6%, 92.8%, and 0.927, respectively, when the signal length was 15. Experimental results of other algorithms showed higher metrics than the proposed algorithm in some cases, but the proposed model showed the fastest inference time.

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Self-motion information is required to keep track of where we are with respect to our environment (spatial updating). Visual signals such as optic flow are relevant to provide information about self-motion, especially in the absence of vestibular and/or proprioceptive cues generated by physical movement. However, the role of optic flow on spatial updating is still debated. A virtual reality system based on a head-mounted display was used to allow participants to experience a self-motion sensation within a naturalistic environment in the absence of physical movement. We asked participants to keep track of spatial positions of a target during simulated self-motion while manipulating the availability of optic flow coming from the lower part of the environment (ground plane). In each trial, the ground could be a green lawn (optic flow ON) or covered in snow (optic flow OFF). We observed that the lack of optic flow on the ground had a detrimental effect on spatial updating. Furthermore, we explored the interaction between the optic flow availability and different characteristics of self-motion: we observed that increasing self-motion speed had a detrimental effect on spatial updating, especially in the absence of optic flow, while self-motion direction (leftward, forward, rightward) and path (translational and curvilinear) had no statically significant effect. Overall, we demonstrated that, in the absence of some idiothetic cues, the optic flow provided by the ground has a dominant role for the self-motion estimation and, hence, for the ability to update the spatial relationships between one's position and the position of the surrounding objects.

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In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem. We further propose and demonstrate that representation-based methods (sparse or collaborative representation) can be used in well-designed regression problems especially over scarce data. To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR). The initial version of CSENs has a proxy mapping stage (i.e., a coarse estimation for the support set) that is required for the input. In this study, we improve the CSEN model by proposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly optimize the so-called proxy mapping stage along with convolutional layers. The experimental evaluations using the KITTI 3D Object Detection distance estimation dataset show that the proposed method can achieve a significantly improved distance estimation performance over all competing methods. Finally, the software implementations of the methods are publicly shared at https://github.com/meteahishali/CSENDistance.

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Parahippocampal cortex (PHC) is a vital neural bases in spatial navigation. However, its functional role is still unclear. "Contextual hypothesis," which assumes that the PHC participates in processing the spatial association between the landmark and destination, provides a potential answer to the question. Nevertheless, the hypothesis was previously tested using the picture categorization task, which is indirectly related to spatial navigation. By now, study is still needed for testing the hypothesis with a navigation-related paradigm. In the current study, we tested the hypothesis by an fMRI experiment in which participants performed a distance estimation task in a virtual environment under three different conditions: landmark free (LF), stable landmark (SL), and ambiguous landmark (AL). By analyzing the behavioral data, we found that the presence of an SL improved the participants' performance in distance estimation. Comparing the brain activity in SL-versus-LF contrast as well as AL-versus-LF contrast, we found that the PHC was activated by the SL rather than by AL when encoding the distance. This indicates that the PHC is elicited by strongly associated context and encodes the landmark reference for distance perception. Furthermore, accessing the representational similarity with the activity of the PHC across conditions, we observed a high similarity within the same condition but low similarity between conditions. This result indicated that the PHC sustains the contextual information for discriminating between scenes. Our findings provided insights into the neural correlates of the landmark information processing from the perspective of contextual hypothesis.

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Neurophysiological advances have given us exciting insights into the systems responsible for spatial mapping in mammals. However, we are still lacking information on the evolution of these systems and whether the underlying mechanisms identified are universal across phyla, or specific to the species studied. Here we address these questions by exploring whether a species that is evolutionarily distant from mammals can perform a task central to mammalian spatial mapping-distance estimation. We developed a behavioural paradigm allowing us to test whether goldfish (Carassius auratus) can estimate distance and explored the behavioural mechanisms that underpin this ability. Fish were trained to swim a set distance within a narrow tank covered with a striped pattern. After changing the background pattern, we found that goldfish use the spatial frequency of their visual environment to estimate distance, doubling the spatial frequency of the background pattern resulted in a large overestimation of the swimming distance. We present robust evidence that goldfish can accurately estimate distance and show that they use local optic flow to do so. These results provide a compelling basis to use goldfish as a model system to interrogate the evolution of the mechanisms that underpin spatial cognition, from brain to behaviour.

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It is well known that financial data frequently contain outlying observations. Almost all methods and techniques used to estimate GARCH models are likelihood-based and thus generally non-robust against outliers. Minimum distance method, as an important tool for statistical inferences and a competitive alternative for achieving robustness, has surprisingly not been well explored for GARCH models. In this paper, we proposed a minimum Hellinger distance estimator (MHDE) and a minimum profile Hellinger distance estimator (MPHDE), depending on whether the innovation distribution is specified or not, for estimating the parameters in GARCH models. The construction and investigation of the two estimators are quite involved due to the non-i.i.d. nature of data. We proved that the MHDE is a consistent estimator and derived its bias in explicit expression. For both of the proposed estimators, we demonstrated their finite-sample performance through simulation studies and compared with the well-established methods including MLE, Gaussian Quasi-MLE, Non-Gaussian Quasi-MLE and Least Absolute Deviation estimator. Our numerical results showed that MHDE and MPHDE have much better performance than MLE-based methods when data are contaminated while simultaneously they are very competitive when data is clean, which testified to the robustness and efficiency of the two proposed MHD-type estimations.

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The social distancing regulations introduced to slow down the spread of COVID-19 virus directly affect a basic form of non-verbal communication, and there may be longer term impacts on human behavior and culture that remain to be analyzed in proxemics studies. To obtain quantitative results for such studies, large media and/or personal photo collections must be analyzed. Several social distance monitoring methods have been proposed for safety purposes, but they are not directly applicable to general photo collections with large variations in the imaging setup. In such studies, the interest shifts from safety to analyzing subtle differences in social distances. Currently, there is no suitable benchmark for developing such algorithms. Collecting images with measured ground-truth pair-wise distances using different camera settings is cumbersome. Moreover, performance evaluation for these algorithms is not straightforward, and there is no widely accepted evaluation protocol. In this paper, we provide an image dataset with measured pair-wise social distances under different camera positions and settings. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate such algorithms. We also propose an automatic social distance estimation method that can be applied on general photo collections. Our method is a hybrid method that combines deep learning-based object detection and human pose estimation with projective geometry. The method can be applied on uncalibrated single images with known focal length and sensor size. The results on our benchmark are encouraging with 91% human detection rate and only 38.24% average relative distance estimation error among the detected people.

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One of the tools for estimating shooting distance is examination of smokeless powder particle distribution on the target area. Components of the powder that are utilized for this purpose are nitrite anions. The traditional method for detecting nitrite anions mainly involves applying the Griess Test. A known-distance test firing is performed with shootings done at several distances from different targets. The color pattern corresponding to nitrite anions from the crime scene is then compared to the patterns obtained from known distances of the test firing. When a mutual shooting takes place at the crime scene, and a shooter also becomes a shootout victim, it is possible that when examining the shooter as a victim (i.e. target), additional nitrite-containing particles, resulting from his/her shooting, will be present on the shooter-victim clothing. This kind of addition may affect the estimation and practically give a shorter-distance estimation comparing to the actual distance. In this paper, an experimental setup was designed in order to understand if nitrite-containing particles were added to a victim as a consequence of him/her being also a shooter. All of the experiments were predominantly designed to try and minimize the effects of other influencing factors and variables in order to examine if the additions resulting from the firing action affect distance estimation. The experiments involved various types of pistols and distances. The results show that in such a scenario, there are marginal additions of nitrite signals on the victim's shirt. Although the forensic expert's final assessment was within the tolerance interval at all distances, caution should be exercised when attempting to estimate shooting distances in scenarios where the victim also shoots.

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