A Software to Detect OCC Emotion, Big-Five Personality and Hofstede Cultural Dimensions of Pedestrians from Video Sequences
This work addresses the challenge of automatically inferring psychological attributes from pedestrian behavior for applications in surveillance or social analysis, but it appears incremental as it combines existing models without major methodological breakthroughs.
The paper tackled the problem of detecting pedestrians' emotions, personalities, and cultural traits from video sequences by analyzing crowd characteristics like group sizes and speeds, and reported promising results in identifying these psychological traits across videos from different countries.
This paper presents a video analysis application to detect personality, emotion and cultural aspects from pedestrians in video sequences, along with a visualizer of features. The proposed model considers a series of characteristics of the pedestrians and the crowd, such as number and size of groups, distances, speeds, among others, and performs the mapping of these characteristics in personalities, emotions and cultural aspects, considering the Cultural Dimensions of Hofstede (HCD), the Big-Five Personality Model (OCEAN) and the OCC Emotional Model. The main hypothesis is that there is a relationship between so-called intrinsic human variables (such as emotion) and the way people behave in space and time. The software was tested in a set of videos from different countries and results seem promising in order to identify these three different levels of psychological traits in the filmed sequences. In addition, the data of the people present in the videos can be seen in a crowd viewer.