TagBook: A Semantic Video Representation without Supervision for Event Detection

We consider the problem of event detection in video for scenarios where only few, or even zero examples are available for training. For this challenging setting, the prevailing solutions in the literature rely on a semantic video representation obtained from thousands of pre-trained concept detectors. Different from existing work, we propose a new semantic video representation that is based on freely available social tagged videos only, without the need for training any intermediate concept detectors. We introduce a simple algorithm that propagates tags from a video's nearest neighbors, similar in spirit to the ones used for image retrieval, but redesign it for video event detection by including video source set refinement and varying the video tag assignment. We call our approach TagBook and study its construction, descriptiveness and detection performance on the TRECVID 2013 and 2014 multimedia event detection datasets and the Columbia Consumer Video dataset. Despite its simple nature, the proposed TagBook video representation is remarkably effective for few-example and zero-example event detection, even outperforming very recent state-of-the-art alternatives building on supervised representations.Comment: accepted for publication as a regular paper in the IEEE Transactions on Multimedi

A Spatio-Temporal Probabilistic Framework for Dividing and Predicting Facial Action Units

This thesis proposed a probabilistic approach to divide the Facial Action Units (AUs) based on the physiological relations and their strengths among the facial muscle groups. The physiological relations and their strengths were captured using a Static Bayesian Network (SBN) from given databases. A data driven spatio-temporal probabilistic scoring function was introduced to divide the AUs into : (i) frequently occurred and strongly connected AUs (FSAUs) and (ii) infrequently occurred and weakly connected AUs (IWAUs). In addition, a Dynamic Bayesian Network (DBN) based predictive mechanism was implemented to predict the IWAUs from FSAUs. The combined spatio-temporal modeling enabled a framework to predict a full set of AUs in real-time. Empirical analyses were performed to illustrate the efficacy and utility of the proposed approach. Four different datasets of varying degrees of complexity and diversity were used for performance validation and perturbation analysis. Empirical results suggest that the IWAUs can be robustly predicted from the FSAUs in real-time and was found to be robust against noise

The THUMOS Challenge on Action Recognition for Videos "in the Wild"

Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on the classification of pre-segmented (i.e., trimmed) videos, which is an artificial task. In THUMOS 2014, we elevated action recognition to a more practical level by introducing temporally untrimmed videos. These also include `background videos' which share similar scenes and backgrounds as action videos, but are devoid of the specific actions. The three editions of the challenge organized in 2013--2015 have made THUMOS a common benchmark for action classification and detection and the annual challenge is widely attended by teams from around the world. In this paper we describe the THUMOS benchmark in detail and give an overview of data collection and annotation procedures. We present the evaluation protocols used to quantify results in the two THUMOS tasks of action classification and temporal detection. We also present results of submissions to the THUMOS 2015 challenge and review the participating approaches. Additionally, we include a comprehensive empirical study evaluating the differences in action recognition between trimmed and untrimmed videos, and how well methods trained on trimmed videos generalize to untrimmed videos. We conclude by proposing several directions and improvements for future THUMOS challenges.Comment: Preprint submitted to Computer Vision and Image Understandin

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

SIFT-ME: A New Feature for Human Activity Recognition

Action representation for robust human activity recognition is still a challenging problem. This thesis proposed a new feature for human activity recognition named SIFT-Motion Estimation (SIFT-ME). SIFT-ME is derived from SIFT correspondences in a sequence of video frames and adds tracking information to describe human body motion. This feature is an extension of SIFT and is used to represent both translation and rotation in plane rotation for the key features. Compare with other features, SIFT-ME is new as it uses rotation of key features to describe action and it robust to the environment changes. Because SIFT-ME is derived from SIFT correspondences, it is invariant to noise, illumination, and small view angle change. It is also invariant to horizontal motion direction due to the embedded tracking information. For action recognition, we use Gaussian Mixture Model to learn motion patterns of several human actions (e.g., walking, running, turning, etc) described by SIFT-ME features. Then, we utilize the maximum log-likelihood criterion to classify actions. As a result, an average recognition rate of 96.6% was achieved using a dataset of 261 videos comprised of six actions performed by seven subjects. Multiple comparisons with existing implementations including optical flow, 2D SIFT and 3D SIFT were performed. The SIFT-ME approach outperforms the other approaches which demonstrate that SIFT-ME is a robust method for human activity recognition

Prosody and Kinesics Based Co-analysis Towards Continuous Gesture Recognition

The aim of this study is to develop a multimodal co-analysis framework for continuous gesture recognition by exploiting prosodic and kinesics manifestation of natural communication. Using this framework, a co-analysis pattern between correlating components is obtained. The co-analysis pattern is clustered using K-means clustering to determine how well the pattern distinguishes the gestures. Features of the proposed approach that differentiate it from the other models are its less susceptibility to idiosyncrasies, its scalability, and simplicity. The experiment was performed on Multimodal Annotated Gesture Corpus (MAGEC) that we created for research on understanding non-verbal communication community, particularly the gestures