Publications

Authors:

Hasan, Irtiza; Setti, Francesco; Tsesmelis, Theodore; Del Bue, Alessio; Galasso, Fabio; Cristani, Marco

Title:

MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses

Year:

2018

Type of item:

Contributo in atti di convegno

Tipologia ANVUR:

Contributo in Atti di convegno

Language:

Inglese

Format:

Elettronico

Congresso:

IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION

Place:

Salt Lake City

Period:

Giugno 2018

Page numbers:

6067-6076

Keyword:

Video surveillance, forecasting, deep learning

Short description of contents:

Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows that adding vislets, that is, short sequences of head pose estimations, allows to increase significantly the trajectory forecasting performance. We then propose to use vislets in a novel framework called MX-LSTM, capturing the interplay between tracklets and vislets thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation. At the same time, MX-LSTM predicts the future head poses, increasing the standard capabilities of the long-term trajectory forecasting approaches. With standard head pose estimators and an attentional-based social pooling, MX-LSTM scores the new trajectory forecasting state-of-the-art in all the considered datasets (Zara01, Zara02, UCY, and TownCentre) with a dramatic margin when the pedestrians slow down, a case where most of the forecasting approaches struggle to provide an accurate solution.

Product ID:

105621

Handle IRIS:

11562/988634

Last Modified:

December 2, 2022

Bibliographic citation:

Hasan, Irtiza; Setti, Francesco; Tsesmelis, Theodore; Del Bue, Alessio; Galasso, Fabio; Cristani, Marco, MX-LSTM: Mixing Tracklets and Vislets to Jointly Forecast Trajectories and Head Poses  in Prooceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , Proceedings of "IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION" , Salt Lake City , Giugno 2018 , 2018 , pp. 6067-6076

Consulta la scheda completa presente nel repository istituzionale della Ricerca di Ateneo