Papers › Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective...

Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition

28 Nov 2016CVPR 2017 7arXiv:1611.09078archive 2025-07-28

Timur Bagautdinov, Alexandre Alahi, François Fleuret, Pascal Fua, Silvio Savarese

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.

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