Deep Structured Models For Group Activity Recognition

Abstract

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recognize the actions of individual people in a scene. Next, a neural-network-based hierarchical graphical model refines the predicted labels for each class by considering dependencies between the classes. This refinement step mimics a message-passing step similar to inference in a probabilistic graphical model. We show that this approach can be effective in group activity recognition, with the deep graphical model improving recognition rates over baseline methods.

References in corpus (1)

• Caffe: Convolutional Architecture for Fast Feature Embedding

Cited by in corpus (6)

• Learning Person Trajectory Representations for Team Activity Analysis
• Convolutional Relational Machine for Group Activity Recognition
• Multi-Level Sequence GAN for Group Activity Recognition
• End-to-end learning potentials for structured attribute prediction
• Latent Embeddings for Collective Activity Recognition
• Active Learning for Structured Prediction from Partially Labeled Data