상세정보
학술대회
Self-Conditional Crowd Activity Detection Network with Multi-label Classification Head
- 발행일
- 202210
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1875-1878
- 협약과제
- 22ZR1100, 자율적으로 연결·제어·진화하는 초연결 지능화 기술 연구, 박준희
- 초록
- In this paper, we proposed new head network architecture in deep neural networks to classify categories for crowd activity. The proposed network was motivated by multi-label classification and conditional generative adversarial networks. In the head network, latent features were transformed into multi-label embedding vectors using pre-trained deep neural networks. The multi-label embedding vectors were regarded as the probability of relevant objects' existence. Then irrelevant embedding components were eliminated by the threshold layer. The refined multi-label embedding vectors are combined with pure latent feature vectors. Finally, a last linear layer predicted crowd activities. The proposed models configured ResNet back-bones with pre-trained weights. In terms of mean accuracy performances, our proposed models showed 1.55% higher in the best case, whereas 0.38% less in the worst case by comparing with baseline models.
- KSP 제안 키워드
- Activity Detection, Deep neural network(DNN), Feature Vector, Label embedding, Latent feature, Linear Layer, Network Architecture, conditional generative adversarial networks, multi-label classification, worst-case