A Detailed Framework for Semantic Description of Humans

Semantic description of humans in images and videos is one of the fundamental problems in computer vision with many applications such as visual surveillance, facial verification, health care, image and video search engines, tagging suggestions and human-computer interaction.

Humans in different shapes.Humans have an outstanding ability when it comes to recognizing (i) semantic attributes such as age, gender, hair style and clothing style (ii) actions such as riding a horse, climbing, running and walking and (iii) facial expressions such as angry, happy and smiling.

We are currently developing novel deep learning solutions for the challenging problem of semantic description of humans in images and videos. The major emphasis is to investigate the challenging generic sub-problems of efficient image and video description, automatic learning of visual models, joint learning from textual annotations and visual data and learning robust methods with minimal supervision.

Researchers

Fahad Khan

Associate Professor, Docent

Michael Felsberg

Professor, Head of Division

External partners

Rao Muhammad Anwer

Publications

A selection of three publications

Fahad Khan, Joost van de Weijer, Rao Muhammad Anwer, Andrew D. Bagdanov, Michael Felsberg, Jorma Laaksonen (2018)

Scale coding bag of deep features for human attribute and action recognition

Machine Vision and Applications , Vol.29 , s.55-71 Continue to DOI

Fahad Khan, Jiaolong Xu, Joost van de Weijer, Andrew D. Bagdanov, Rao Muhammad Anwer, Antonio M. Lopez (2015)

Recognizing Actions Through Action-Specific Person Detection

IEEE Transactions on Image Processing , Vol.24 , s.4422-4432 Continue to DOI

Rao Muhammad Anwer, Fahad Khan, Jorma Laaksonen (2018)

Two-Stream Part-based Deep Representation for Human Attribute Recognition

2018 INTERNATIONAL CONFERENCE ON BIOMETRICS (ICB) , s.90-97 Continue to DOI

WASP research at CVL

Visual Object Tracking

Visual Object Tracking is one of the principal challenges in Computer Vision, where the task is to locate a certain object in all frames of a video, given only its location in the first frame.

Machine Learning for Visual Navigation and Localization

Navigation and localization are principal challenges in Robot Vision, where the task is to estimate the geometric relation between a vehicle and its environment, either passively in a virtual model or actively through controlling the robot.

Point Cloud Processing

With the growing availability of depth cameras and lidar, the processing of the resulting point clouds has become its own sub-area of Computer Vision.

WASP Computer Vision Laboratory

The research covers a broad range of topics within artificial visual systems with a particular focus on machine learning for robot vision. The group has made major contributions in establishing the concept of computer vision in Sweden.