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Context Change Detection for an Ultra-Low Power Low-Resolution Ego-Vision Imager
- Source :
- Lecture Notes in Computer Science ISBN: 9783319466033, ECCV Workshops (1)
- Publication Year :
- 2016
- Publisher :
- Springer International Publishing, 2016.
-
Abstract
- With the increasing popularity of wearable cameras, such as GoPro or Narrative Clip, research on continuous activity monitoring from egocentric cameras has received a lot of attention. Research in hardware and software is devoted to find new efficient, stable and long-time running solutions; however, devices are too power-hungry for truly always-on operation, and are aggressively duty-cycled to achieve acceptable lifetimes. In this paper we present a wearable system for context change detection based on an egocentric camera with ultra-low power consumption that can collect data 24/7. Although the resolution of the captured images is low, experimental results in real scenarios demonstrate how our approach, based on Siamese Neural Networks, can achieve visual context awareness. In particular, we compare our solution with hand-crafted features and with state of art technique and propose a novel and challenging dataset composed of roughly 30000 low-resolution images.
- Subjects :
- 030506 rehabilitation
Artificial neural network
Computer science
business.industry
Deep learning
Computer Science (all)
Wearable computer
Context (language use)
02 engineering and technology
Low-resolution
Theoretical Computer Science
03 medical and health sciences
Software
Deep Learning
0202 electrical engineering, electronic engineering, information engineering
Context awareness
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
Egocentric vision
ULP camera
0305 other medical science
business
Change detection
Subjects
Details
- Language :
- English
- ISBN :
- 978-3-319-46603-3
- ISBNs :
- 9783319466033
- Database :
- OpenAIRE
- Journal :
- Lecture Notes in Computer Science ISBN: 9783319466033, ECCV Workshops (1)
- Accession number :
- edsair.doi.dedup.....16829e8895a4da62a86f92e8d8623b22