Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity RecognitionReportar como inadecuado




Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition - Descarga este documento en PDF. Documentación en PDF para descargar gratis. Disponible también para leer online.

Presented at: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, July 21-26, 2017 Publication date: 2017

We present a unified framework for understanding human social behaviors in raw image sequences. Our model jointly detects multiple individuals, infers their social actions, and estimates the collective actions with a single feed-forward pass through a neural network. We propose a single architecture that does not rely on external detection algorithms but rather is trained end-to-end to generate dense proposal maps that are refined via a novel inference scheme. The temporal consistency is handled via a person-level matching Recurrent Neural Network. The complete model takes as input a sequence of frames and outputs detections along with the estimates of individual actions and collective activities. We demonstrate state-of-the-art performance of our algorithm on multiple publicly available benchmarks.

Reference EPFL-CONF-230241





Autor: Bagautdinov, Timur; Alahi, Alexandre; Fleuret, François; Fua, Pascal; Savarese, Silvio

Fuente: https://infoscience.epfl.ch/record/230241?ln=en







Documentos relacionados