3D Single-Person Concurrent Activity Detection Using Stacked Relation Network
نویسندگان
چکیده
منابع مشابه
Simultaneous Multi-Person Detection and Single-Person Pose Estimation With a Single Heatmap Regression Network
We propose a two component fully-convolutional network for heatmap regression to perform multi-person pose estimation from images. The first component of the network predicts all body joints of all persons visible on an image, while the second component groups these body joints based on the position of the head of the person of interest. By applying the second component for all detected heads, ...
متن کامل3D Obstacle Detection Using a Single Camera
This paper aims at detecting obstacles using a single camera in an unknown three dimensional world, for 3D motion of an unmanned air vehicle. Obstacle detection is a pre-requisite for collision-free motion of a UAV through 3D space. Most research towards vision based obstacle detection and avoidance has been done for 2D planar motion of ground robots and using active sensors like laser range fi...
متن کاملPerson authentication using 3D palmprints
Biometric technologies, including fingerprint, face, iris, and palmprint recognition, are drawing increasing attention in both academic research and industrial applications.1 Palmprint identification uses the inner surface of the palm to distinguish between people2 and includes among its many merits stable features, a high level of accuracy, and user-friendliness. To the best of our knowledge, ...
متن کاملDetection of Single and Dual Incipient Process Faults Using an Improved Artificial Neural Network
Changes in the physicochemical conditions of process unit, even under control, may lead to what are generically referred to as faults. The cognition of causes is very important, because the system can be diagnosed and fault tolerated. In this article, we discuss and propose an artificial neural network that can detect the incipient and gradual faults either individually or mutually. The mai...
متن کاملContextual Multi-Scale Region Convolutional 3D Network for Activity Detection
Activity detection is a fundamental problem in computer vision. Detecting activities of different temporal scales is particularly challenging. In this paper, we propose the contextual multi-scale region convolutional 3D network (CMSRC3D) for activity detection. To deal with the inherent temporal scale variability of activity instances, the temporal feature pyramid is used to represent activitie...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i07.6917