Latent Embeddings for Collective Activity Recognition
| Autor: | Jian-Fang Hu, Peizhen Zhang, Yongyi Tang, Wei-Shi Zheng |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
FOS: Computer and information sciences
Relation (database) business.industry Computer science Feature vector Deep learning Computer Vision and Pattern Recognition (cs.CV) Feature extraction Computer Science - Computer Vision and Pattern Recognition 02 engineering and technology Latent variable 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Activity recognition 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence Graphical model business computer 0105 earth and related environmental sciences |
| Zdroj: | AVSS |
| DOI: | 10.48550/arxiv.1709.06770 |
| Popis: | Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ- ous state-of-the-art methods using hand-crafted potentials in conventional graphical model which can only define a limited range of relations. Thus, the complex structural de- pendencies among individuals involved in a collective sce- nario cannot be fully modeled. In this paper, we overcome these limitations by embedding latent variables into feature space and learning the feature mapping functions in a deep learning framework. The embeddings of latent variables build a global relation containing person-group interac- tions and richer contextual information by jointly modeling broader range of individuals. Besides, we assemble atten- tion mechanism during embedding for achieving more com- pact representations. We evaluate our method on three col- lective activity datasets, where we contribute a much larger dataset in this work. The proposed model has achieved clearly better performance as compared to the state-of-the- art methods in our experiments. Comment: 6pages, accepted by IEEE-AVSS2017 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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