Instructor activity recognition through deep spatiotemporal features and feedforward Extreme Learning Machines

Human action recognition has the potential to predict the activities of an instructor within the lecture room. Evaluation of lecture delivery can help teachers analyze shortcomings and plan lectures more effectively. However, manual or peer evaluation is time-consuming, tedious and sometimes it is difficult to remember all the details of the lecture. Therefore, automation of lecture delivery evaluation significantly improves teaching style. In this paper, we propose a feedforward learning model for instructor's activity recognition in the lecture room. The proposed scheme represents a video sequence in the form of a single frame to capture the motion profile of the instructor by observing the spatiotemporal relation within the video frames. First, we segment the instructor silhouettes from input videos using graph-cut segmentation and generate a motion profile. These motion profiles are centered by obtaining the largest connected components and normalized. Then, these motion profiles are represented in the form of feature maps by a deep convolutional neural network. Then, an extreme learning machine (ELM) classifier is trained over the obtained feature representations to recognize eight different activities of the instructor within the classroom. For the evaluation of the proposed method, we created an instructor activity video (IAVID-1) dataset and compared our method against different state-of-the-art activity recognition methods. Furthermore, two standard datasets, MuHAVI and IXMAS, were also considered for the evaluation of the proposed scheme.We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for research work carried in Centre for Computer Vision Research (C2VR) at University of Engineering and Technology Taxila, Pakistan. Sergio A Velastin acknowledges funding by the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509), and Banco Santander.We are also very thankful to participants, faculty, and postgraduate students of Computer Engineering Department who took part in the data acquisition phase.Without their consent, this work was not possible

Video anomaly detection and localization by local motion based joint video representation and OCELM

Nowadays, human-based video analysis becomes increasingly exhausting due to the ubiquitous use of surveillance cameras and explosive growth of video data. This paper proposes a novel approach to detect and localize video anomalies automatically. For video feature extraction, video volumes are jointly represented by two novel local motion based video descriptors, SL-HOF and ULGP-OF. SL-HOF descriptor captures the spatial distribution information of 3D local regions’ motion in the spatio-temporal cuboid extracted from video, which can implicitly reflect the structural information of foreground and depict foreground motion more precisely than the normal HOF descriptor. To locate the video foreground more accurately, we propose a new Robust PCA based foreground localization scheme. ULGP-OF descriptor, which seamlessly combines the classic 2D texture descriptor LGP and optical flow, is proposed to describe the motion statistics of local region texture in the areas located by the foreground localization scheme. Both SL-HOF and ULGP-OF are shown to be more discriminative than existing video descriptors in anomaly detection. To model features of normal video events, we introduce the newly-emergent one-class Extreme Learning Machine (OCELM) as the data description algorithm. With a tremendous reduction in training time, OCELM can yield comparable or better performance than existing algorithms like the classic OCSVM, which makes our approach easier for model updating and more applicable to fast learning from the rapidly generated surveillance data. The proposed approach is tested on UCSD ped1, ped2 and UMN datasets, and experimental results show that our approach can achieve state-of-the-art results in both video anomaly detection and localization task.This work was supported by the National Natural Science Foundation of China (Project nos. 60970034, 61170287, 61232016)

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Elephant cognition in primate perspective

On many of the staple measures of comparative psychology, elephants show no obvious differences from other mammals, such as primates: discrimination learning, memory, spontaneous tool use, etc. However, a range of more naturalistic measures have recently suggested that elephant cognition may be rather different. Wild elephants sub-categorize humans into groups, independently making this classification on the basis of scent or colour. In number discrimination, elephants show no effects of absolute magnitude or relative size disparity in making number judgements. In the social realm, elephants show empathy into the problems faced by others, and give hints of special abilities in cooperation, vocal imitation and perhaps teaching. Field data suggest that the elephant’s vaunted reputation for memory may have a factual basis, in two ways. Elephants’ ability to remember large-scale space over long periods suggests good cognitive mapping skills. Elephants’ skill in keeping track of the current locations of many family members implies that working memory may be unusually developed, consistent with the laboratory finding that their quantity judgements do not show the usual magnitude effects.Publisher PDFPeer reviewe

The Influence Exposure Has on Consumer Behavior

Advertising has been around for thousands of years and has proven to be a valuable asset to company revenue. The methods used in advertising have been examined closely more recently, specifically from a psychological standpoint. The human brain reacts to advertisements in different ways. Low and high involvement advertisements stimulate the brain in the subconscious and conscious state effectively. While each of these advertising methods are useful, complex messaging techniques seem to stimulate recall more effectively than simple messaging. In the age of digital advertising, sponsors look to put new resources to use to ensure paid advertisements are doing their job. With data mining and location based services available, advertisers are getting more in touch with the consumer than ever before. The bombardment of advertising is changing the consumer’s eye for low and high involvement and the advertising industry will be forced to adjust yet again

Machine learning approaches to video activity recognition: from computer vision to signal processing

244 p.La investigación presentada se centra en técnicas de clasificación para dos tareas diferentes, aunque relacionadas, de tal forma que la segunda puede ser considerada parte de la primera: el reconocimiento de acciones humanas en vídeos y el reconocimiento de lengua de signos.En la primera parte, la hipótesis de partida es que la transformación de las señales de un vídeo mediante el algoritmo de Patrones Espaciales Comunes (CSP por sus siglas en inglés, comúnmente utilizado en sistemas de Electroencefalografía) puede dar lugar a nuevas características que serán útiles para la posterior clasificación de los vídeos mediante clasificadores supervisados. Se han realizado diferentes experimentos en varias bases de datos, incluyendo una creada durante esta investigación desde el punto de vista de un robot humanoide, con la intención de implementar el sistema de reconocimiento desarrollado para mejorar la interacción humano-robot.En la segunda parte, las técnicas desarrolladas anteriormente se han aplicado al reconocimiento de lengua de signos, pero además de ello se propone un método basado en la descomposición de los signos para realizar el reconocimiento de los mismos, añadiendo la posibilidad de una mejor explicabilidad. El objetivo final es desarrollar un tutor de lengua de signos capaz de guiar a los usuarios en el proceso de aprendizaje, dándoles a conocer los errores que cometen y el motivo de dichos errores

The BG News January 28, 2008

The BGSU campus student newspaper January 28, 2008. Volume 98 - Issue 91https://scholarworks.bgsu.edu/bg-news/8867/thumbnail.jp