Hough Forests for Object Detection, Tracking, and Action Recognition

2011 ◽  
Vol 33 (11) ◽  
pp. 2188-2202 ◽  
Author(s):  
J. Gall ◽  
A. Yao ◽  
N. Razavi ◽  
L. Van Gool ◽  
V. Lempitsky
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
Hough Forests

Hierarchical Hough forests for view-independent action recognition

2016 ◽  
Author(s):  
Barbara Hilsenbeck ◽  
David Munch ◽  
Hilke Kieritz ◽  
Wolfgang Hubner ◽  
Michael Arens
Keyword(s):  
Action Recognition ◽  
Independent Action ◽  
Hough Forests

Repetitive assembly action recognition based on object detection and pose estimation

2020 ◽  
Vol 55 ◽  
pp. 325-333 ◽  
Author(s):  
Chengjun Chen ◽  
Tiannuo Wang ◽  
Dongnian Li ◽  
Jun Hong
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
Pose Estimation

scholarly journals Development of a Wearable Camera and AI Algorithm for Medication Behavior Recognition

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3594
Author(s):  
Hwiwon Lee ◽  
Sekyoung Youm
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
Health Professionals ◽  
Medication Compliance ◽  
Image Data ◽  
Image Sensor ◽  
Training Dataset ◽  
Behavior Recognition ◽  
Recognition Model ◽  
Detection Model

As many as 40% to 50% of patients do not adhere to long-term medications for managing chronic conditions, such as diabetes or hypertension. Limited opportunity for medication monitoring is a major problem from the perspective of health professionals. The availability of prompt medication error reports can enable health professionals to provide immediate interventions for patients. Furthermore, it can enable clinical researchers to modify experiments easily and predict health levels based on medication compliance. This study proposes a method in which videos of patients taking medications are recorded using a camera image sensor integrated into a wearable device. The collected data are used as a training dataset based on applying the latest convolutional neural network (CNN) technique. As for an artificial intelligence (AI) algorithm to analyze the medication behavior, we constructed an object detection model (Model 1) using the faster region-based CNN technique and a second model that uses the combined feature values to perform action recognition (Model 2). Moreover, 50,000 image data were collected from 89 participants, and labeling was performed on different data categories to train the algorithm. The experimental combination of the object detection model (Model 1) and action recognition model (Model 2) was newly developed, and the accuracy was 92.7%, which is significantly high for medication behavior recognition. This study is expected to enable rapid intervention for providers seeking to treat patients through rapid reporting of drug errors.

scholarly journals Visual Feature Learning on Video Object and Human Action Detection: A Systematic Review

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 72
Author(s):  
Dengshan Li ◽  
Rujing Wang ◽  
Peng Chen ◽  
Chengjun Xie ◽  
Qiong Zhou ◽  
...  
Keyword(s):  
Object Detection ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Detection Methods ◽  
Video Object ◽  
Action Detection ◽  
Video Frames ◽  
Video Detection ◽  
Human Action Detection

Video object and human action detection are applied in many fields, such as video surveillance, face recognition, etc. Video object detection includes object classification and object location within the frame. Human action recognition is the detection of human actions. Usually, video detection is more challenging than image detection, since video frames are often more blurry than images. Moreover, video detection often has other difficulties, such as video defocus, motion blur, part occlusion, etc. Nowadays, the video detection technology is able to implement real-time detection, or high-accurate detection of blurry video frames. In this paper, various video object and human action detection approaches are reviewed and discussed, many of them have performed state-of-the-art results. We mainly review and discuss the classic video detection methods with supervised learning. In addition, the frequently-used video object detection and human action recognition datasets are reviewed. Finally, a summarization of the video detection is represented, e.g., the video object and human action detection methods could be classified into frame-by-frame (frame-based) detection, extracting-key-frame detection and using-temporal-information detection; the methods of utilizing temporal information of adjacent video frames are mainly the optical flow method, Long Short-Term Memory and convolution among adjacent frames.

scholarly journals Discriminative Hough Forests for Object Detection

2012 ◽  
Author(s):  
Paul Wohlhart ◽  
Samuel Schulter ◽  
Martin Köstinger ◽  
Peter Roth ◽  
Horst Bischof
Keyword(s):  
Object Detection ◽  
Hough Forests
Sign in / Sign up

Export Citation Format

Share Document