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

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.


Published in:
30Th Ieee Conference On Computer Vision And Pattern Recognition (Cvpr 2017), 3425-3434
Presented at:
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Hawaii, USA, July 21-26, 2017
Year:
2017
Publisher:
New York, Ieee
ISSN:
1063-6919
ISBN:
978-1-5386-0457-1
Laboratories:




 Record created 2017-08-21, last modified 2018-03-18

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