Simple yet efficient real-time pose-based action recognition
| Autor: | Cristóbal Curio, Thomas Gulde, Dennis Ludl |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Training set Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition 020207 software engineering Context (language use) 02 engineering and technology Space (commercial competition) Pipeline (software) Machine Learning (cs.LG) Image (mathematics) Action (philosophy) Simple (abstract algebra) 0202 electrical engineering electronic engineering information engineering Action recognition 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | ITSC |
| Popis: | Recognizing human actions is a core challenge for autonomous systems as they directly share the same space with humans. Systems must be able to recognize and assess human actions in real-time. In order to train corresponding data-driven algorithms, a significant amount of annotated training data is required. We demonstrated a pipeline to detect humans, estimate their pose, track them over time and recognize their actions in real-time with standard monocular camera sensors. For action recognition, we encode the human pose into a new data format called Encoded Human Pose Image (EHPI) that can then be classified using standard methods from the computer vision community. With this simple procedure we achieve competitive state-of-the-art performance in pose-based action detection and can ensure real-time performance. In addition, we show a use case in the context of autonomous driving to demonstrate how such a system can be trained to recognize human actions using simulation data. Submitted to IEEE Intelligent Transportation Systems Conference (ITSC) 2019. Code will be available soon at https://github.com/noboevbo/ehpi_action_recognition |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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