scholarly journals Chord-Length Shape Features for Human Activity Recognition

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Samy Sadek ◽  
Ayoub Al-Hamadi ◽  
Bernd Michaelis ◽  
Usama Sayed
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Action Recognition ◽  
Shape Descriptor ◽  
Human Action ◽  
Human Activity Recognition ◽  
Chord Length ◽  
Shape Features ◽  
Computationally Efficient ◽  
Fuzzy Membership Functions

Despite their high stability and compactness, chord-length shape features have received relatively little attention in the human action recognition literature. In this paper, we present a new approach for human activity recognition, based on chord-length shape features. The most interesting contribution of this paper is twofold. We first show how a compact, computationally efficient shape descriptor; the chord-length shape features are constructed using 1-D chord-length functions. Second, we unfold how to use fuzzy membership functions to partition action snippets into a number of temporal states. On two benchmark action datasets (KTH and WEIZMANN), the approach yields promising results that compare favorably with those previously reported in the literature, while maintaining real-time performance.

scholarly journals Patient Monitoring by Abnormal Human Activity Recognition Based on CNN Architecture

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 1993
Author(s):  
Malik Ali Gul ◽  
Muhammad Haroon Yousaf ◽  
Shah Nawaz ◽  
Zaka Ur Rehman ◽  
HyungWon Kim
Keyword(s):  
Activity Recognition ◽  
Action Recognition ◽  
Human Activity ◽  
Patient Monitoring ◽  
Human Action Recognition ◽  
Confidence Score ◽  
Human Action ◽  
Human Activity Recognition ◽  
Video Sequences ◽  
Human Actions

Human action recognition has emerged as a challenging research domain for video understanding and analysis. Subsequently, extensive research has been conducted to achieve the improved performance for recognition of human actions. Human activity recognition has various real time applications, such as patient monitoring in which patients are being monitored among a group of normal people and then identified based on their abnormal activities. Our goal is to render a multi class abnormal action detection in individuals as well as in groups from video sequences to differentiate multiple abnormal human actions. In this paper, You Look only Once (YOLO) network is utilized as a backbone CNN model. For training the CNN model, we constructed a large dataset of patient videos by labeling each frame with a set of patient actions and the patient’s positions. We retrained the back-bone CNN model with 23,040 labeled images of patient’s actions for 32 epochs. Across each frame, the proposed model allocated a unique confidence score and action label for video sequences by finding the recurrent action label. The present study shows that the accuracy of abnormal action recognition is 96.8%. Our proposed approach differentiated abnormal actions with improved F1-Score of 89.2% which is higher than state-of-the-art techniques. The results indicate that the proposed framework can be beneficial to hospitals and elder care homes for patient monitoring.

Human Action Recognition Using Median Background and Max Pool Convolution with Nearest Neighbor

2019 ◽  
Vol 10 (2) ◽  
pp. 34-47 ◽  
Author(s):  
Bagavathi Lakshmi ◽  
S.Parthasarathy
Keyword(s):  
Machine Learning ◽  
Activity Recognition ◽  
Action Recognition ◽  
Human Activity ◽  
Nearest Neighbor ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Activity Recognition ◽  
Machine Learning Algorithms ◽  
Support Vector

Discovering human activities on mobile devices is a challenging task for human action recognition. The ability of a device to recognize its user's activity is important because it enables context-aware applications and behavior. Recently, machine learning algorithms have been increasingly used for human action recognition. During the past few years, principal component analysis and support vector machines is widely used for robust human activity recognition. However, with global dynamic tendency and complex tasks involved, this robust human activity recognition (HAR) results in error and complexity. To deal with this problem, a machine learning algorithm is proposed and explores its application on HAR. In this article, a Max Pool Convolution Neural Network based on Nearest Neighbor (MPCNN-NN) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics. The MPCNN-NN framework for HAR consists of three steps. In the first step, for each activity, the features of interest or foreground frame are detected using Median Background Subtraction. The second step consists of organizing the features (i.e. postures) that represent the strongest generic discriminating features (i.e. postures) based on Max Pool. The third and the final step is the HAR based on Nearest Neighbor that postures which maximizes the probability. Experiments have been conducted to demonstrate the superiority of the proposed MPCNN-NN framework on human action dataset, KARD (Kinect Activity Recognition Dataset).

scholarly journals Deep Learning Based Human Activity Recognition Using Spatio-Temporal Image Formation of Skeleton Joints

2021 ◽  
Vol 11 (6) ◽  
pp. 2675
Author(s):  
Nusrat Tasnim ◽  
Mohammad Khairul Islam ◽  
Joong-Hwan Baek
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Action Recognition ◽  
Human Activity ◽  
Image Formation ◽  
Human Action Recognition ◽  
Human Action ◽  
Human Activity Recognition ◽  
3D Skeleton ◽  
Spatio Temporal

Human activity recognition has become a significant research trend in the fields of computer vision, image processing, and human–machine or human–object interaction due to cost-effectiveness, time management, rehabilitation, and the pandemic of diseases. Over the past years, several methods published for human action recognition using RGB (red, green, and blue), depth, and skeleton datasets. Most of the methods introduced for action classification using skeleton datasets are constrained in some perspectives including features representation, complexity, and performance. However, there is still a challenging problem of providing an effective and efficient method for human action discrimination using a 3D skeleton dataset. There is a lot of room to map the 3D skeleton joint coordinates into spatio-temporal formats to reduce the complexity of the system, to provide a more accurate system to recognize human behaviors, and to improve the overall performance. In this paper, we suggest a spatio-temporal image formation (STIF) technique of 3D skeleton joints by capturing spatial information and temporal changes for action discrimination. We conduct transfer learning (pretrained models- MobileNetV2, DenseNet121, and ResNet18 trained with ImageNet dataset) to extract discriminative features and evaluate the proposed method with several fusion techniques. We mainly investigate the effect of three fusion methods such as element-wise average, multiplication, and maximization on the performance variation to human action recognition. Our deep learning-based method outperforms prior works using UTD-MHAD (University of Texas at Dallas multi-modal human action dataset) and MSR-Action3D (Microsoft action 3D), publicly available benchmark 3D skeleton datasets with STIF representation. We attain accuracies of approximately 98.93%, 99.65%, and 98.80% for UTD-MHAD and 96.00%, 98.75%, and 97.08% for MSR-Action3D skeleton datasets using MobileNetV2, DenseNet121, and ResNet18, respectively.

scholarly journals kNN Prototyping Schemes for Embedded Human Activity Recognition with Online Learning

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 96
Author(s):  
Paulo J. S. Ferreira ◽  
João M. P. Cardoso ◽  
João Mendes-Moreira
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Maximum Size ◽  
Search Space ◽  
Human Activity Recognition ◽  
Locality Sensitive Hashing ◽  
Computationally Efficient ◽  
Online Mode ◽  
Online Classification ◽  
Memory Constraints

The kNN machine learning method is widely used as a classifier in Human Activity Recognition (HAR) systems. Although the kNN algorithm works similarly both online and in offline mode, the use of all training instances is much more critical online than offline due to time and memory restrictions in the online mode. Some methods propose decreasing the high computational costs of kNN by focusing, e.g., on approximate kNN solutions such as the ones relying on Locality-Sensitive Hashing (LSH). However, embedded kNN implementations also need to address the target device’s memory constraints, especially as the use of online classification needs to cope with those constraints to be practical. This paper discusses online approaches to reduce the number of training instances stored in the kNN search space. To address practical implementations of HAR systems using kNN, this paper presents simple, energy/computationally efficient, and real-time feasible schemes to maintain at runtime a maximum number of training instances stored by kNN. The proposed schemes include policies for substituting the training instances, maintaining the search space to a maximum size. Experiments in the context of HAR datasets show the efficiency of our best schemes.

scholarly journals Tracking a Subset of Skeleton Joints: An Effective Approach towards Complex Human Activity Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Muhammad Latif Anjum ◽  
Stefano Rosa ◽  
Basilio Bona
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Human Robot Interaction ◽  
Support Vector ◽  
Computationally Efficient ◽  
Feature Vectors ◽  
Kinect Camera ◽  
Multiclass Classifier ◽  
Selection Of

We present a robust algorithm for complex human activity recognition for natural human-robot interaction. The algorithm is based on tracking the position of selected joints in human skeleton. For any given activity, only a few skeleton joints are involved in performing the activity, so a subset of joints contributing the most towards the activity is selected. Our approach of tracking a subset of skeleton joints (instead of tracking the whole skeleton) is computationally efficient and provides better recognition accuracy. We have developed both manual and automatic approaches for the selection of these joints. The position of the selected joints is tracked for the duration of the activity and is used to construct feature vectors for each activity. Once the feature vectors have been constructed, we use a Support Vector Machines (SVM) multiclass classifier for training and testing the algorithm. The algorithm has been tested on a purposely built dataset of depth videos recorded using Kinect camera. The dataset consists of 250 videos of 10 different activities being performed by different users. Experimental results show classification accuracy of 83% when tracking all skeleton joints, 95% when using manual selection of subset joints, and 89% when using automatic selection of subset joints.

Sign in / Sign up

Export Citation Format

Share Document