Group Activity Recognition With Differential Recurrent Convolutional Neural Networks

Abstract

Human group activity recognition has drawn the attention of researchers worldwide because of the significant role it plays in many applications, including video surveillance and public security. Existing solutions for group activity recognition rely on human detection and tracking. To ensure high detection accuracy, current state-of-the-art tracking techniques require human supervision to identify objects of interest before automatic tracking can take place. This limitation has prevented existing approaches from being used in real-world applications. In scenarios when human supervision is unavailable, tracking algorithms could generate inaccurate trajectories and cause a decrease in performance for the existing group analysis methods. To address the aforementioned drawbacks, we investigate in this paper an end-to-end deep model, Differential Recurrent Convolutional Neural Networks (DRCNN). Our model consists of convolutional neural networks (CNN) and stacked differential long short-term memory (DLSTM) networks. It takes sequential raw video data as input and does not consider each group member as an individual object. Different from traditional non-end-to-end solutions which separate the steps of feature extraction and parameter learning, DRCNN utilizes a unified deep model to optimize the parameters of CNN and LSTM hand in hand. It thus has the potential of generating a more harmonious model. In addition, taking advantage of the semantic representation of CNN and the memory states of DLSTM, DRCNN has strong capabilities in understanding complex scene semantics and group dynamics. Extensive experimental studies indicate that the proposed technique can accomplish the task of fully automatic group activity recognition without sacrificing performance, and even outperforms the human-aided state-ofthe- art methods on two benchmark group activity datasets. To the best of our knowledge, this is the first end-to-end group activity recognition technique ever proposed.

Publication Date

6-28-2017

Publication Title

Proceedings - 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017 - 1st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heterogeneous Face Recognition, HFR 2017, Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation, DCER and HPE 2017 and 3rd Facial Expression Recognition and Analysis Challenge, FERA 2017

Number of Pages

526-531

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/FG.2017.70

Socpus ID

85026301683 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85026301683

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