Dual Viewpoint Passenger State Classification Using 3D CNNs
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- Type
- article
- Published
- 2018-04-16
- Cited by
- 4
- References
- 23
- Access
- Open access
- OpenAlex
- https://openalex.org/W2884851201
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:53024240
Keywords
Dual (grammatical number), Computer science, State (computer science), Artificial intelligence, Pattern recognition (psychology)
References
- UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
- Towards Good Practices for Very Deep Two-Stream ConvNets
- Learning Spatiotemporal Features with 3D Convolutional Networks
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- ActivityNet: A large-scale video benchmark for human activity understanding
- Deeply learned attributes for crowded scene understanding
- Vision based hand gesture recognition for human computer interaction: a survey
- Recognition of driving postures by contourlet transform and random forests
- Going deeper with convolutions
- HMDB: A large video database for human motion recognition
- Two-Stream Convolutional Networks for Action Recognition in Videos
- Deep Residual Learning for Image Recognition
- Long-Term Temporal Convolutions for Action Recognition
- Driving posture recognition by convolutional neural networks
- Recognizing driver inattention by convolutional neural networks
- Aggregated Residual Transformations for Deep Neural Networks
- Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
- The Kinetics Human Action Video Dataset
- Real-time Distracted Driver Posture Classification
- Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
Cited by
- Abnormal Activity Detection and Classification of Bus Passengers With In-Vehicle Image Sensing
- Research on passengers behavior recognition method in public transport vehicles based on efficient 3D CNN
- A Review of Machine Learning and Deep Learning Methods for Person Detection, Tracking and Identification, and Face Recognition with Applications
- A comprehensive review of on-board action recognition models in public transportation systems
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