ACM MM 2020 Paper Interest Recognition from Posture and Behaviour

Wolmer Bigi, Claudio Baecchi, Alberto Del Bimbo, Automatic Interest Recognition from Posture and Behaviour

Our paper Wolmer Bigi, Claudio Baecchi, Alberto Del Bimbo, Automatic Interest Recognition from Posture and Behaviour has been accepted for publication at ACM Multimedia 2020.

In the last years, the clothing industry has attracted a lot of interest from researchers. Increasing research efforts have been devoted into giving the buyer a way to improve the shopping experience by suggesting meaningful items to purchase.

These efforts result in works aiming at suggesting good matches for clothes, but seem to lack one important aspect: understanding the user’s interest. In fact, to suggest something it is first necessary to collect the user’s personal interests, or something about his or her previous purchases. Without this information, no personalized suggestion can be made.

User interest understanding allows to recognize if a user is showing interest in a product he or she is looking at, acquiring precious information that can be later leveraged. Usually user interest is associated to facial expressions, but these are known to be easily falsifiable. Moreover, when privacy is a concern, faces are often impossible to exploit.

To address all these aspects, we propose an automatic system that aims to recognize the user’s interest towards a garment by just looking at body posture and behaviour.

To train and evaluate our system we create a body pose interest dataset, named BodyInterest, which consists of 30 users looking at garments for a total of approximately 6 hours of videos. Extensive evaluations show the effectiveness of our proposed method.

Yahoo YStar!

donation of 98 servers by Yahoo YSTAR

Francesco Gelli

Lecture by Francesco Gelli

Better Understanding of Actionable Images

CBMI 2017

CBMI 2017 19-21 june 2017
Firenze, Italy

My Kieu, Andrew D. Bagdanov, Marco Bertini, Alberto del Bimbo, Task-conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery

ECCV 2020 Paper

Domain Adaptation for Pedestrian Detection