Human Interaction Prediction Using Deep Temporal Features

Predicting human interaction is challenging as the on-going activity has to be inferred based on a partially observed video. Essentially, a good algorithm should effectively model the mutual influence between the two interacting subjects. Also, only a small region in the scene is discriminative for identifying the on-going interaction. In this work, we propose a relative attention model to explicitly address these difficulties. Built on a tri-coupled deep recurrent structure representing both interacting subjects and global interaction status, the proposed network collects spatio-temporal information from each subject, rectified with global interaction information, yielding effective interaction representation. Moreover, the proposed network also unifies an attention module to assign higher importance to the regions which are relevant to the on-going action. Extensive experiments have been conducted on two public datasets, and the results demonstrate that the proposed relative attention network successfully predicts informative regions between interacting subjects, which in turn yields superior human interaction prediction accuracy.

Download Full-text

Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction

IEEE Transactions on Multimedia ◽

10.1109/tmm.2017.2778559 ◽

2018 ◽

Vol 20 (7) ◽

pp. 1712-1723 ◽

Cited By ~ 18

Author(s):

Qiuhong Ke ◽

Mohammed Bennamoun ◽

Senjian An ◽

Ferdous Sohel ◽

Farid Boussaid

Keyword(s):

Human Interaction ◽

Interaction Prediction ◽

Structural Context ◽

Ranking Score ◽

Score Fusion ◽

Context Models

Download Full-text

Temporal Characteristics of Speech in Simultaneous Communication

Journal of Speech Language and Hearing Research ◽

10.1044/jshr.3805.1014 ◽

1995 ◽

Vol 38 (5) ◽

pp. 1014-1024 ◽

Cited By ~ 26

Author(s):

Robert L. Whitehead ◽

Nicholas Schiavetti ◽

Brenda H. Whitehead ◽

Dale Evan Metz

Keyword(s):

Pause Duration ◽

Vowel Duration ◽

Vowel Perception ◽

Temporal Characteristics ◽

Spoken English ◽

Temporal Features ◽

Before And After ◽

Word Duration ◽

Rate Of Speech ◽

Speech Condition

The purpose of this investigation was twofold: (a) to determine if there are changes in specific temporal characteristics of speech that occur during simultaneous communication, and (b) to determine if known temporal rules of spoken English are disrupted during simultaneous communication. Ten speakers uttered sentences consisting of a carrier phrase and experimental CVC words under conditions of: (a) speech, (b) speech combined with signed English, and (c) speech combined with signed English for every word except the CVC word that was fingerspelled. The temporal features investigated included: (a) sentence duration, (b) experimental CVC word duration, (c) vowel duration in experimental CVC words, (d) pause duration before and after experimental CVC words, and (e) consonantal effects on vowel duration. Results indicated that for all durational measures, the speech/sign/fingerspelling condition was longest, followed by the speech/sign condition, with the speech condition being shortest. It was also found that for all three speaking conditions, vowels were longer in duration when preceding voiced consonants than vowels preceding their voiceless cognates, and that a low vowel was longer in duration than a high vowel. These findings indicate that speakers consistently reduced their rate of speech when using simultaneous communication, but did not violate these specific temporal rules of English important for consonant and vowel perception.

Download Full-text

Review of Interpersonal Dynamics: Essays and Readings on Human Interaction.

Contemporary Psychology ◽

10.1037/0010364 ◽

1969 ◽

Vol 14 (5) ◽

pp. 303-303

Author(s):

H. J. LEAVITT

Keyword(s):

Human Interaction ◽

Interpersonal Dynamics

Download Full-text