arXiv:1512.03131
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
원문 초록
Deep learning has recently achieved very promising results in a wide range of areas such as computer vision, speech recognition and natural language processing. It aims to learn hierarchical representations of data by using deep architecture models. In a smart city, a lot of data (e.g. videos captured from many distributed sensors) need to be automatically processed and analyzed. In this paper, we review the deep learning algorithms applied to video analytics of smart city in terms of different research topics: object detection, object tracking, face recognition, image classification and scene labeling.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Towards Reliable Detection of Empty Space: Conditional Marked Point Processes for Object Detection
A Real-Time System for Egocentric Hand-Object Interaction Detection in Industrial Domains
ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
DuET: Dual Incremental Object Detection via Exemplar-Free Task Arithmetic
Universal Fine-grained Visual Categorization by Concept Guided Learning
DecoupleNet: A Lightweight Backbone Network With Efficient Feature Decoupling for Remote Sensing Visual Tasks
토론
이 논문과 연결된 포럼 글이 아직 없습니다.