Machine detection of human-object interaction in images and videos

phys.org | 6/17/2019 | Staff
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Jia-Bin Huang, assistant professor in the Bradley Department of Electrical and Computer Engineering and a faculty member at the Discovery Analytics Center, has received a Google Faculty Research Award to support his work in detecting human-object interaction in images and videos.

The Google award, which is in the Machine Perception category, will allow Huang to tackle the challenges of detecting two aspects of human-object interaction: modeling the relationship between a person and relevant objects/scene for gathering contextual information and mining hard examples automatically from unlabeled but interaction-rich videos.

Huang - Progress - Classifying - Detecting - Objects

According to Huang, while significant progress has been made in classifying, detecting, and segmenting objects, representing images/videos as a collection of isolated object instances has failed to capture the information essential for understanding activity.

"By improving the model and scaling up the training, we aim to move a step further toward building socially intelligent machines," Huang said.

Image - Video - Goal - Persons - Instances

Given an image or a video, the goal is to localize persons and object instances, as well as recognize interaction, if any, between each pair of a person and an object. This provides a structured representation of a visually grounded graph over the humans and the object instances they interact with.

For example: Two men are next to each other on the sidelines of a tennis court, one standing up and holding an umbrella and one sitting on a chair holding a tennis racquet and looking at a bag on the ground beside him....
(Excerpt) Read more at: phys.org
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