Aesthetic Highlight Detection in Movies Based on Synchronization of Spectators’ Reactions.

Detection of aesthetic highlights is a challenge for understanding the affective processes taking place during movie watching. In this paper we study spectators’ responses to movie aesthetic stimuli in a social context. Moreover, we look for uncovering the emotional component of aesthetic highlights in movies. Our assumption is that synchronized spectators’ physiological and behavioral reactions occur during these highlights because: (i) aesthetic choices of filmmakers are made to elicit specific emotional reactions (e.g. special effects, empathy and compassion toward a character, etc.) and (ii) watching a movie together causes spectators’ affective reactions to be synchronized through emotional contagion. We compare different approaches to estimation of synchronization among multiple spectators’ signals, such as pairwise, group and overall synchronization measures to detect aesthetic highlights in movies. The results show that the unsupervised architecture relying on synchronization measures is able to capture different properties of spectators’ synchronization and detect aesthetic highlights based on both spectators’ electrodermal and acceleration signals. We discover that pairwise synchronization measures perform the most accurately independently of the category of the highlights and movie genres. Moreover, we observe that electrodermal signals have more discriminative power than acceleration signals for highlight detection

Towards Segmentation and Labelling of Motion Data in Manufacturing Scenarios

Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.There is a significant interest to evaluate the occupational exposure that manufacturing operators are subjected throughout the working day. The objective evaluation of occupational exposure with direct measurements and the need for automatic annotation of relevant events arose. The current work proposes the use of a self similarity matrix (SSM) as a tool to flag events that may be of importance to be analyzed by ergonomic teams. This way, data directly retrieved from the work environment will be summarized and segmented into sub-sequences of interest over a multi-timescale approach. The process occurs under 3 timescale levels: Active working periods, working cycles, and in-cycle activities. The novelty function was used to segment non-active and active working periods with an F1-score of 95%. while the similarity function was used to correctly segment 98% of working cycle with a duration error of 6.12%. In addition, this method was extended into examples of multi time scale segmentation with the intent of providing a summary of a time series as well as support in data labeling tasks, by means of a query-by-example process to detect all subsequences.authorsversionpublishe

Audio-assisted movie dialogue detection

An audio-assisted system is investigated that detects if a movie scene is a dialogue or not. The system is based on actor indicator functions. That is, functions which define if an actor speaks at a certain time instant. In particular, the cross-correlation and the magnitude of the corresponding the cross-power spectral density of a pair of indicator functions are input to various classifiers, such as voted perceptions, radial basis function networks, random trees, and support vector machines for dialogue/non-dialogue detection. To boost classifier efficiency AdaBoost is also exploited. The aforementioned classifiers are trained using ground truth indicator functions determined by human annotators for 41 dialogue and another 20 non-dialogue audio instances. For testing, actual indicator functions are derived by applying audio activity detection and actor clustering to audio recordings. 23 instances are randomly chosen among the aforementioned 41 dialogue instances, 17 of which correspond to dialogue scenes and 6 to non-dialogue ones. Accuracy ranging between 0.739 and 0.826 is reported. © 2008 IEEE