Set Of Videos Research Articles

A Finite State Machine Fall Detection Using Quadrilateral Shape Features

A video-based fall detection system was presented; which consists of data acquisition, image processing, feature extraction, feature selection, classification and finite state machine. A two-dimensional human posture image was represented by 12 features extracted from the generalisation of a silhouette shape to a quadrilateral. The corresponding feature vectors for three groups of human pose were statistically analysed by using a non-parametric Kruskal Wallis test to assess the different significance level between them. From the statistical test, non-significant features were discarded. Four selected kernel-based Support Vector Machine: linear, quadratics, cubic and Radial Basis Function classifiers were trained to classify three human posture groups. Among four classifiers, the last one performed the best in terms of performance matric on testing set. The classifier outperformed others with high achievement ofaverage sensitivity, precision and F-score of 99.19%, 99.25% and 99.22%, respectively. Such pose classification model output was further used in a simple finite state machine to trigger the falling event alarms. The fall detection system was tested on different fall video sets and able to detect the presence offalling events in a frame sequence of videos with accuracy of 97.32% and low computional time.

Full-Reference Stability Assessment of Digital Video Stabilization Based on Riemannian Metric.

Assessing the quality of the motion stability is important to evaluating the performance of video stabilization algorithms. This paper presents a novel quality assessment scheme for the video motion stability in a full-reference (FR) manner. Given ideally stable videos and their corresponding shaky videos, our method measures the geodesic distance between motion paths of the stable and the stabilized videos. Due to the use of the Riemannian metric defined on the manifold of spatial transformations, our method enables the intrinsic and faithful measurement on pairwise motion disparities. To facilitate the FR assessment, a data set of stable and shaky videos is constructed by directly capturing realistic stable/shaky videos with a customized device. Then, digital video stabilization algorithms can be run on shaky videos to obtain the stabilized sequence of frames, whereupon their performances are evaluated by using our stability assessment. The experiments demonstrate that our stability assessment gains good concordance with the subjective assessment.

Truly Reconciling the Case of Jahi McMath.

This article clarifies some issues raised by Dr. Ariane Lewis in her recent "Current Opinion/Arguments" article on the case of Jahi McMath. Review of case materials. Jahi's case most likely represents an instance of global ischemic penumbra (GIP) mimicking brain death (BD), with intracranial blood flow too low to support neuronal function or to be detected by radionuclide scan but sufficient to prevent widespread necrosis. Her MRI scan 9months after the ischemic insult showed gross preservation of cortical and internal structures, incompatible with there ever having been a period of completely absent blood flow. Regarding Jahi's alleged intermittent responsiveness, the set of videos, unsystematic as they are, constitutes convincing evidence that her movements in seeming response to command are not of spinal cord origin and are indeed voluntary responses, placing her in the category of minimally conscious state (MCS). In the absence of serial examinations by experts in MCS, the benefit of the doubt should be given. Unfortunately, her death on June 22, 2018, 4½ years after the diagnosis of BD, precludes such examinations. During those 4½ years, Jahi underwent menarche, with three documented menstrual periods, and ongoing pubertal development. Her case is an important example of false-positive diagnosis of BD, demonstrating the inability of current diagnostic standards to distinguish true BD from potentially reversible brain nonfunction due to GIP. The incidence of such mimicry is impossible to determine, because in most cases a BD diagnosis becomes a self-fulfilling prophecy.

Are you with me? Probing the human capacity to recognize external/internal attention in others’ faces

ABSTRACTFacial cues provide information about affective states and the direction of attention that is important for human social interaction. The present study examined how this capacity extends to judging whether attention is internally or externally directed. Participants evaluated a set of videos and images showing the face of people focused externally on a task, or internally while they performed a task in imagination. We found that participants could identify the focus of attention above chance in videos, and to a lesser degree in static images, but only when the eye region was visible. Self-reports further indicated that participants relied particularly on the eye region in their judgements. Interestingly, people engaged in demanding cognitive tasks were more likely judged to be externally focused independent of the actual focus of attention. These findings demonstrate that humans use information from the face and especially from the eyes of others not only to infer external goals or actions, but also to detect when others focus internally on their own thoughts and feelings.

Full video pulse extraction.

This paper introduces a new method to automate heart-rate detection using remote photoplethysmography (rPPG). The method replaces the commonly used region of interest (RoI) detection and tracking, and does not require initialization. Instead, it combines a number of candidate pulse-signals computed in the parallel, each biased towards differently colored objects in the scene. The method is based on the observation that the temporally averaged colors of video objects (skin and background) are usually quite stable over time in typical application-driven scenarios, such as the monitoring of a subject sleeping in bed, or an infant in an incubator. The resulting system, called full video pulse extraction (FVP), allows the direct use of raw video streams for pulse extraction. Our benchmark set of diverse videos shows that FVP enables long-term sleep monitoring in visible light and in infrared, and works for adults and neonates. Although we only demonstrate the concept for heart-rate monitoring, we foresee the adaptation to a range of vital signs, thus benefiting the larger video health monitoring field.

Surprise and destabilize: prediction error influences episodic memory reconsolidation.

Through the process of “reconsolidation,” reminders can temporarily destabilize memories and render them vulnerable to change. Recent rodent research has proposed that prediction error, or the element of surprise, is a key component of this process; yet, this hypothesis has never before been extended to complex episodic memories in humans. In our novel paradigm, we used naturalistic stimuli to demonstrate that prediction error enables adaptive updating of episodic memories. In Study 1, participants (N = 48) viewed 18 videos, each depicting an action–outcome event. The next day, we reactivated these memories by presenting the videos again. We found that incomplete reminders, which interrupted videos before the outcome, made memories vulnerable to subsequent interference from a new set of videos, producing false memories. In Study 2 (N = 408), an independent sample rated qualities of the stimuli. We found that videos that were more surprising when interrupted produced more false memories. Last, in Study 3 (N = 24), we tested competing predictions of reconsolidation theory and the Temporal Context Model, an alternative account of source confusion. Consistent with the mechanistic time-course of reconsolidation, our effects were crucially time-dependent. Overall, we synthesize prior animal and human research to present compelling evidence that prediction error destabilizes episodic memories and drives dynamic updating in the face of new information.

'A New You, That\u2019s Who': an evaluation of short videos on puberty and human reproduction

Learning basic information about puberty and human reproduction can alleviate concerns about the transition to adolescence and provide a foundation for later learning about more advanced reproductive health topics, such as family planning. Parents and children alike believe educational videos make these topics more engaging, and socio-cultural theory suggests such videos can effectively promote learning. To that end, we tested the efficacy of a series of reproductive health education videos called 'A New You, That’s Who', with a sample of 80 11-year-old children in a research laboratory setting. The series was designed with socio-cultural theory learning principles in mind and consisted of three 5-min animated music videos focused on puberty, reproductive anatomy, and menstruation. Children were randomly assigned to watch the 'A New You, That’s Who' videos or a control set of videos on the scientific method. Children who watched the treatment videos out-performed peers in the control group on a measure reproductive health knowledge. However, there were no differences in attitudes towards puberty between conditions. The videos provide effective ways to learn factual information about puberty and human reproduction, and may be valuable tools to supplement social-emotional lessons provided at home, at school, or in other real-world settings.

Detection of Lane-Change Events in Naturalistic Driving Videos.

Lane changes are important behaviors to study in driving research. Automated detection of lane-change events is required to address the need for data reduction of a vast amount of naturalistic driving videos. This paper presents a method to deal with weak lane-marker patterns as small as a couple of pixels wide. The proposed method is novel in its approach to detecting lane-change events by accumulating lane-marker candidates over time. Since the proposed method tracks lane markers in temporal domain, it is robust to low resolution and many different kinds of interferences. The proposed technique was tested using 490 h of naturalistic driving videos collected from 63 drivers. The lane-change events in a 10-h video set were first manually coded and compared with the outcome of the automated method. The method's sensitivity was 94.8% and the data reduction rate was 93.6%. The automated procedure was further evaluated using the remaining 480-h driving videos. The data reduction rate was 97.4%. All 4971 detected events were manually reviewed and classified as either true or false lane-change events. Bootstrapping showed that the false discovery rate from the larger data set was not significantly different from that of the 10-h manually coded data set. This study demonstrated that the temporal processing of lane markers is an effcient strategy for detecting lane-change events involving weak lane-marker patterns in naturalistic driving.

Resilience of Video Streaming Services to Network Impairments

When dealing with networks, performance management through conventional quality of service (QoS)-based methods becomes difficult and is often ineffective. In fact, quality emerges as an end-to-end factor, for it is particularly sensitive to the end-user perception of the overall service, i.e., the user’s quality of experience (QoE). However, the two are not independent from each other and their relationship has to be studied through metrics that go beyond the typical network parameters. To better explore the value of assessing QoE alongside QoS in high-speed, lossy networks, this paper presents an experimental methodology to understand the relation between network QoS onto service QoE, with the aim to perform a combined network-service assessment. Using video streaming services as the test-case (given their extended usage nowadays), in this paper, we provide studies on three network-impaired video-sets with the aim to provide a comprehensive evaluation of the effects of networks on video quality. First, the ReTRIeVED video set provides the means to understand the most impairing effects on networks. Furthermore, it triggered the idea to create our own sets, specialized in the most impairing conditions for 2-D and 3-D: the LIMP Video Quality Database and the 3-D-HEVC-Net Video Quality Database. Our study and methodology are meant to provide service providers with the means to pinpoint the working boundaries of their video-sets in face of different network conditions. At the same time, network operators may use our findings to predict how network control policies affect the user’s perception of the service.

Parent Involvement in the Getting Ready for School Intervention Is Associated With Changes in School Readiness Skills.

The role of parent involvement in school readiness interventions is not well-understood. The Getting Ready for School (GRS) intervention is a novel program that has both home and school components and aims to improve early literacy, math, and self-regulatory skills in preschool children from socioeconomically disadvantaged families. In this study, we first examined associations between family characteristics and different indices of parent involvement in the GRS intervention. We then examined associations between parent involvement and change in children's school readiness skills over time. Participants were 133 preschool children attending Head Start and their parents who participated in the GRS intervention during the academic year 2014–2015. Parent involvement was operationalized as attendance to GRS events at the school, time spent at home doing GRS activities, and usage of digital program materials, which included a set of videos to support the implementation of parent-child activities at home. Although few family characteristics were significantly associated with parent involvement indices, there was a tendency for some markers of higher socioeconomic status to be linked with greater parent involvement. In addition, greater parent involvement in the GRS intervention was significantly associated with greater gains in children's early literacy, math, and self-regulatory skills. These findings suggest that parent involvement in comprehensive early interventions could be beneficial in terms of improving school readiness for preschoolers from disadvantaged families.

Egocentric Meets Top-View.

Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones, and glasses, we have access to a plethora of videos captured from first person perspective. Surveillance cameras and Unmanned Aerial Vehicles (UAVs) also offer tremendous amounts of video data recorded from top and oblique view points. Egocentric and surveillance vision have been studied extensively but separately in the computer vision community. The relationship between these two domains, however, remains unexplored. In this study, we make the first attempt in this direction by addressing two basic yet challenging questions. First, having a set of egocentric videos and a top-view surveillance video, does the top-view video contain all or some of the egocentric viewers? In other words, have these videos been shot in the same environment at the same time? Second, if so, can we identify the egocentric viewers in the top-view video? These problems can become extremely challenging when videos are not temporally aligned. Each view, egocentric or top, is modeled by a graph and the assignment and time-delays are computed iteratively using the spectral graph matching framework. We evaluate our method in terms of ranking and assigning egocentric viewers to identities present in the top-view video over a dataset of 50 top-view and 188 egocentric videos captured under different conditions. We also evaluate the capability of our proposed approaches in terms of temporal alignment. The experiments and results demonstrate the capability of the proposed approaches in terms of jointly addressing the temporal alignment and assignment tasks.

Seeing action simulation as it unfolds: The implicit effects of action scenes on muscle contraction evidenced through the use of a grip-force sensor

Action simulation is a cognitive process that mentally simulates a motor act without performing it in the true external world. Simulation mechanisms play a key role in perceiving, feeling and understanding actions executed by others. However, very little is known about the process dynamics because of the absence of a behavioral tool to probe directly the action simulation process as it unfolds.Twenty-seven healthy adults were required to hold a force sensor in a relaxed pinch-grip while viewing action videos of different intensities: wait (null); touch (low); move (medium); crush (high). When contrasting the variations in grip force (GFv) across conditions, results indicated that GFv started to increase and peaked respectively 200 and 400 ms after the moment of effector-object contact. In the wait condition, GFv remained flat throughout the trial confirming an absence of simulation engagement. Peak GFv was greater for the high and medium than for the low intensity videos suggesting greater brain activity overflow to the peripheral motor system when simulating more effortful body movements. These effects were negatively correlated with the motor imagery abilities of the participants, with greater GFv in the poor imagers as determined by the Movement Imagery Questionnaire.Our results confirm the possibility of using a non-invasive grip force sensor to detect not only when individuals are cognitively engaged in action simulation but also to reveal the dynamics of the process. With various sets of videos, this paradigm offers new perspectives in the study of action simulation and its role in human cognition.

Tracking Occluded Objects and Recovering Incomplete Trajectories by Reasoning About Containment Relations and Human Actions

This paper studies a challenging problem of tracking severely occluded objects in long video sequences. The proposed method reasons about the containment relations and human actions, thus infers and recovers occluded objects identities while contained or blocked by others. There are two conditions that lead to incomplete trajectories: i) Contained. The occlusion is caused by a containment relation formed between two objects, e.g., an unobserved laptop inside a backpack forms containment relation between the laptop and the backpack. ii) Blocked. The occlusion is caused by other objects blocking the view from certain locations, during which the containment relation does not change. By explicitly distinguishing these two causes of occlusions, the proposed algorithm formulates tracking problem as a network flow representation encoding containment relations and their changes. By assuming all the occlusions are not spontaneously happened but only triggered by human actions, an MAP inference is applied to jointly interpret the trajectory of an object by detection in space and human actions in time. To quantitatively evaluate our algorithm, we collect a new occluded object dataset captured by Kinect sensor, including a set of RGB-D videos and human skeletons with multiple actors, various objects, and different changes of containment relations. In the experiments, we show that the proposed method demonstrates better performance on tracking occluded objects compared with baseline methods.

Utilizing text recognition for the defects extraction in sewers CCTV inspection videos

This paper proposed a novel automated framework for analyzing and tracking sewer inspection close-circuit television (CCTV) videos. The proposed model mainly supports the off-site examination and quality management process of the videos and enables efficient revaluation of CCTV videos to extract sewer condition data. The study discusses an important module for any automated analysis and defect detection in CCTV video. It includes two main modules: text recognition and cracks extraction. In the first module, multi-frame integration (MFI) was applied to reduce the background complexity, time and computational requirements needed for the video processing. Then maximally stable extremal regions (MSER) was used on the grayscale channel and HSV channel to effectively detect all the text edges. Saturation color channel was also applied to verify the detected text line and remove false alarms. In the second module, by utilizing the text information on each frame, the operator’s operation during the inspection is simulated which would indicate valuable clues about the location and severity of the cracks. The proposed methodology was validated using a set of video provided by the Korea Institute of Construction Technology.

Conveying facial expressions to blind and visually impaired persons through a wearable vibrotactile device.

In face-to-face social interactions, blind and visually impaired persons (VIPs) lack access to nonverbal cues like facial expressions, body posture, and gestures, which may lead to impaired interpersonal communication. In this study, a wearable sensory substitution device (SSD) consisting of a head mounted camera and a haptic belt was evaluated to determine whether vibrotactile cues around the waist could be used to convey facial expressions to users and whether such a device is desired by VIPs for use in daily living situations. Ten VIPs (mean age: 38.8, SD: 14.4) and 10 sighted persons (SPs) (mean age: 44.5, SD: 19.6) participated in the study, in which validated sets of pictures, silent videos, and videos with audio of facial expressions were presented to the participant. A control measurement was first performed to determine how accurately participants could identify facial expressions while relying on their functional senses. After a short training, participants were asked to determine facial expressions while wearing the emotion feedback system. VIPs using the device showed significant improvements in their ability to determine which facial expressions were shown. A significant increase in accuracy of 44.4% was found across all types of stimuli when comparing the scores of the control (mean±SEM: 35.0±2.5%) and supported (mean±SEM: 79.4±2.1%) phases. The greatest improvements achieved with the support of the SSD were found for silent stimuli (68.3% for pictures and 50.8% for silent videos). SPs also showed consistent, though not statistically significant, improvements while supported. Overall, our study shows that vibrotactile cues are well suited to convey facial expressions to VIPs in real-time. Participants became skilled with the device after a short training session. Further testing and development of the SSD is required to improve its accuracy and aesthetics for potential daily use.

Quantitative videographic analysis of intraoperative total shoulder arthroplasty is predictive of radiographic implant loosening

BackgroundIt is believed that both patient and surgeon factors contribute to premature implant loosening. This video study was designed to answer the following questions: Can orthopedic surgeons reliably differentiate between procedures done well and those that will lead to early glenoid failure? Do the difficulty of the operation and the surgeon's performance predict a patient's outcome? Does the presence of a Walch B2 glenoid result in surgery that is evidently more difficult and performed in such a way to suggest early glenoid component failure?MethodsEleven upper extremity surgeons blindly graded a set of intraoperative videos of 15 total shoulder arthroplasty patients (grouped by outcome at 2 years). Evaluation questionnaires consisted of questions about the perceived difficulty and the surgeon's performance. Total and partial patient scores were calculated for each video. Higher calculated score would indicate worse postsurgical outcome.ResultsThe loosening group had a significantly higher total score (P = .0057). Also, patients with B2 glenoids scored significantly higher than patients with other wear type. The analysis of overall procedure performance indicated difference between outcome groups (P = .0063).ConclusionOur results indicate that surgeons could review surgical videos and differentiate the cases that were difficult or those that were more likely to lead to loosening of the glenoid component. The presence of a B2 glenoid was predictive of difficult surgery. The results of this study should serve as a starting point for surgeons interested in critically evaluating performance and also for those interested in finding ways to maximize patient outcomes after total shoulder arthroplasty.

Visual Persons Behavior Diary Generation Model based on Trajectories and Pose Estimation

The behavior pattern of persons was the important output of the surveillance analysis. This paper focus on the generation model of visual person behavior diary. The pipeline includes the person detection, tracking, and the person behavior classify. This paper adopts the deep convolutional neural model YOLO (You Only Look Once)V2 for person detection module. Multi person tracking was based on the detection framework. The Hungarian assignment algorithm was used to the matching. The person appearance model was integrated by HSV color model and Hash code model. The person object motion was estimated by the Kalman Filter. The multi objects were matching with exist tracklets through the appearance and motion location distance by the Hungarian assignment method. A long continuous trajectory for one person was get by the spatial-temporal continual linking algorithm. And the face recognition information was used to identify the trajectory. The trajectories with identification information can be used to generate the visual diary of person behavior based on the scene context information and person action estimation. The relevant modules are tested in public data sets and our own capture video sets. The test results show that the method can be used to generate the visual person behavior pattern diary with certain accuracy.

Energy Level-Based Abnormal Crowd Behavior Detection.

The change of crowd energy is a fundamental measurement for describing a crowd behavior. In this paper, we present a crowd abnormal detection method based on the change of energy-level distribution. The method can not only reduce the camera perspective effect, but also detect crowd abnormal behavior in time. Pixels in the image are treated as particles, and the optical flow method is adopted to extract the velocities of particles. The qualities of different particles are distributed as different value according to the distance between the particle and the camera to reduce the camera perspective effect. Then a crowd motion segmentation method based on flow field texture representation is utilized to extract the motion foreground, and a linear interpolation calculation is applied to pedestrian’s foreground area to determine their distance to the camera. This contributes to the calculation of the particle qualities in different locations. Finally, the crowd behavior is analyzed according to the change of the consistency, entropy and contrast of the three descriptors for co-occurrence matrix. By calculating a threshold, the timestamp when the crowd abnormal happens is determined. In this paper, multiple sets of videos from three different scenes in UMN dataset are employed in the experiment. The results show that the proposed method is effective in characterizing anomalies in videos.

Closing the gap: connecting sudden representational change to the subjective Aha! experience in insightful problem solving.

Two hallmarks of insightful problem solving are thought to be suddenness in the emergence of solution due to changes in problem representation, and the subjective Aha! experience. Although a number of studies have explored the Aha! experience, few studies have attempted to measure representational change. Following the lead of Durso et al. (Psychol Sci 5(2):94-97, 1994) and Cushen and Wiley (Conscious Cognit 21(3):1166-1175, 2012), in this study, participants made importance-to-solution ratings throughout their solution attempts as a way to assess representational change. Participants viewed a set of magic trick videos with the task of finding out how each trick worked, and rated six action verbs for each trick (including one that implied the correct solution) multiple times during solution. They were also asked to indicate the extent to which they experienced an Aha! moment. Patterns of ratings that showed a sudden change towards a correct solution led to stronger Aha! experiences than patterns that showed a more incremental change towards a correct solution, or a change towards incorrect solutions. The results show a connection between sudden changes in problem representations (leading to correct solutions) and the subjective appraisal of solutions as an Aha! experience. This offers the first empirical support for a close relationship between two theoretical constructs that have traditionally been assumed to be related to insightful problem solving.

Multi-Object Tracking by Flying Cameras Based on a Forward-Backward Interaction

The automatic analysis of images acquired by cameras mounted on board of drones (flying cameras) is attracting many scientists working in the field of computer vision; the interest is related to the increasing need of algorithms able to understand the scenes acquired by flying cameras, by detecting the moving objects, calculating their trajectories, and finally understanding their activities. The problem is made challenging by the fact that, in the most general case, the drone flies without any awareness of the environment; thus, no initial set-up configuration based on the appearance of the area of interest can be used for simplifying the task, as it generally happens when working with fixed cameras. Moreover, the apparent movements of the objects in the images are superimposed to that generated by the camera, associated with the flight of the drone (varying in the altitude, speed, and the angles of yaw and pitch). Finally, it has to be considered that the algorithm should involve simple visual computational models as the drone can only host embedded computers having limited computing resources. This paper proposes a detection and tracking algorithm based on a novel paradigm suitably combining a forward tracking based on local data association with a backward chain, aimed at automatically tuning the operating parameters frame by frame, so as to be totally independent on the visual appearance of the flying area. This also definitively drops any time-consuming manual configuration procedure by a human operator. Although the method is self-configured and requires low-computational resources, its accuracy on a wide data set of real videos demonstrates its applicability in real contexts, even running over embedded platforms. Experimental results are given on a set of 53 videos and more than 60 000 frames.