Are all the frames equally important?

In this work, we address the problem of measuring and predicting temporal video saliency - a metric which defines the importance of a video frame for human attention. Unlike the conventional spatial saliency which defines the location of the salient regions within a frame (as it is done for still images), temporal saliency considers importance of a frame as a whole and may not exist apart from context. The proposed interface is an interactive cursor-based algorithm for collecting experimental data about temporal saliency. We collect the first human responses and perform their analysis. As a result, we show that qualitatively, the produced scores have very explicit meaning of the semantic changes in a frame, while quantitatively being highly correlated between all the observers. Apart from that, we show that the proposed tool can simultaneously collect fixations similar to the ones produced by eye-tracker in a more affordable way. Further, this approach may be used for creation of first temporal saliency datasets which will allow training computational predictive algorithms. The proposed interface does not rely on any special equipment, which allows to run it remotely and cover a wide audience.Comment: CHI'20 Late Breaking Work

Visual Importance-Biased Image Synthesis Animation

Present ray tracing algorithms are computationally intensive, requiring hours of computing time for complex scenes. Our previous work has dealt with the development of an overall approach to the application of visual attention to progressive and adaptive ray-tracing techniques. The approach facilitates large computational savings by modulating the supersampling rates in an image by the visual importance of the region being rendered. This paper extends the approach by incorporating temporal changes into the models and techniques developed, as it is expected that further efficiency savings can be reaped for animated scenes. Applications for this approach include entertainment, visualisation and simulation

Look at Me: Early Gaze Engagement Enhances Corticospinal Excitability During Action Observation

Direct gaze is a powerful social cue able to capture the onlooker's attention. Beside gaze, head and limb movements as well can provide relevant sources of information for social interaction. This study investigated the joint role of direct gaze and hand gestures on onlookers corticospinal excitability (CE). In two experiments we manipulated the temporal and spatial aspects of observed gaze and hand behavior to assess their role in affecting motor preparation. To do this, transcranial magnetic stimulation (TMS) on the primary motor cortex (M1) coupled with electromyography (EMG) recording was used in two experiments. In the crucial manipulation, we showed to participants four video clips of an actor who initially displayed eye contact while starting a social request gesture, and then completed the action while directing his gaze toward a salient object for the interaction. This way, the observed gaze potentially expressed the intention to interact. Eye tracking data confirmed that gaze manipulation was effective in drawing observers' attention to the actor's hand gesture. In the attempt to reveal possible time-locked modulations, we tracked CE at the onset and offset of the request gesture. Neurophysiological results showed an early CE modulation when the actor was about to start the request gesture looking straight to the participants, compared to when his gaze was averted from the gesture. This effect was time-locked to the kinematics of the actor's arm movement. Overall, data from the two experiments seem to indicate that the joint contribution of direct gaze and precocious kinematic information, gained while a request gesture is on the verge of beginning, increases the subjective experience of involvement and allows observers to prepare for an appropriate social interaction. On the contrary, the separation of gaze cues and body kinematics can have adverse effects on social motor preparation. CE is highly susceptible to biological cues, such as averted gaze, which is able to automatically capture and divert observer's attention. This point to the existence of heuristics based on early action and gaze cues that would allow observers to interact appropriately

Automated drowsiness detection for improved driving safety

Several approaches were proposed for the detection and prediction of drowsiness. The approaches can be categorized as estimating the fitness of duty, modeling the sleep-wake rhythms, measuring the vehicle based performance and online operator monitoring. Computer vision based online operator monitoring approach has become prominent due to its predictive ability of detecting drowsiness. Previous studies with this approach detect driver drowsiness primarily by making preassumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to datamine actual human behavior during drowsiness episodes. Automatic classifiers for 30 facial actions from the Facial Action Coding system were developed using machine learning on a separate database of spontaneous expressions. These facial actions include blinking and yawn motions, as well as a number of other facial movements. In addition, head motion was collected through automatic eye tracking and an accelerometer. These measures were passed to learning-based classifiers such as Adaboost and multinomial ridge regression. The system was able to predict sleep and crash episodes during a driving computer game with 96% accuracy within subjects and above 90% accuracy across subjects. This is the highest prediction rate reported to date for detecting real drowsiness. Moreover, the analysis revealed new information about human behavior during drowsy drivin

Effects of Impedance Reduction of a Robot for Wrist Rehabilitation on Human Motor Strategies in Healthy Subjects during Pointing Tasks

Studies on human motor control demonstrated the existence of simplifying strategies (namely `Donders' law') adopted to deal with kinematically redundant motor tasks. In recent research we showed that Donders' law also holds for human wrist during pointing tasks, and that it is heavily perturbed when interacting with a highly back-drivable state-of-the-art rehabilitation robot. We hypothesized that this depends on the excessive mechanical impedance of the Pronation/Supination (PS) joint of the robot and in this work we analyzed the effects of its reduction. To this end we deployed a basic force control scheme, which minimizes human-robot interaction force. This resulted in a 70% reduction of the inertia in PS joint and in decrease of 81% and 78% of the interaction torques during 1-DOF and 3-DOFs tasks. To assess the effects on human motor strategies, pointing tasks were performed by three subjects with a lightweight handheld device, interacting with the robot using its standard PD control (setting impedance to zero) and with the force-controlled robot. We quantified Donders' law as 2-dimensional surfaces in the 3-dimensional configuration space of rotations. Results revealed that the subject-specific features of Donders' surfaces reappeared after the reduction of robot impedance obtained via the force control