Due to the importance of object detection in video analysis and image annotation, it is widely utilized in a number of computer vision tasks such as face recognition, autonomous vehicles, activity recognition, tracking objects and identity verification. Object detection does not only involve classification and identification of objects within images, but also involves localizing and tracing the objects by creating bounding boxes around the objects and labelling them with their respective prediction scores. Here, we leverage and discuss how connected vision systems can be used to embed cameras, image processing, Edge Artificial Intelligence (AI), and data connectivity capabilities for flood label detection. We favored the engineering definition of label detection that a label is a sequence of discrete measurable observations obtained using a capturing device such as web cameras, smart phone, etc. We built a Big Data service of around 1000 images (image annotation service) including the image geolocation information from various flooding events in the Carolinas (USA) with a total of eight different object categories. Our developed platform has several smart AI tools and task configurations that can detect objects’ edges or contours which can be manually adjusted with a threshold setting so as to best segment the image.more »
Intelligent Image Collection: Building the Optimal Dataset
Key recognition tasks such as fine-grained visual categorization (FGVC) have benefited from increasing attention among computer vision researchers. The development and evaluation of new approaches relies heavily on benchmark datasets; such datasets are generally built primarily with categories that have images readily available, omitting categories with insufficient data. This paper takes a step back and rethinks dataset construction, focusing on intelligent image collection driven by: (i) the inclusion of all desired categories, and, (ii) the recognition performance on those categories. Based on a small, author-provided initial dataset, the proposed system recommends which categories the authors should prioritize collecting additional images for, with the intent of optimizing overall categorization accuracy. We show that mock datasets built using this method outperform datasets built without such a guiding framework. Additional experiments give prospective dataset creators intuition into how, based on their circumstances and goals, a dataset should be constructed.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- 2020 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Sponsoring Org:
- National Science Foundation
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