On integration of multiple features for human activity recognition in video sequences

2021 ◽  
Author(s):  
Arati Kushwaha ◽  
Ashish Khare ◽  
Prashant Srivastava
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Video Sequences ◽  
Multiple Features

Towards Intelligent Human Behavior Detection for Video Surveillance

2018 ◽  
pp. 34-73 ◽  
Author(s):  
Swati Nigam ◽  
Rajiv Singh ◽  
A. K. Misra
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Behavior ◽  
Human Activity Recognition ◽  
Human Detection ◽  
Video Sequences ◽  
Human Tracking ◽  
Feature Descriptor ◽  
Behavior Detection ◽  
Human Behavior Analysis

Computer vision techniques are capable of detecting human behavior from video sequences. Several state-of-the-art techniques have been proposed for human behavior detection and analysis. However, a collective framework is always required for intelligent human behavior analysis. Therefore, in this chapter, the authors provide a comprehensive understanding towards human behavior detection approaches. The framework of this chapter is based on human detection, human tracking, and human activity recognition, as these are the basic steps of human behavior detection process. The authors provide a detailed discussion over the human behavior detection framework and discuss the feature-descriptor-based approach. Furthermore, they have provided qualitative and quantitative analysis for the detection framework and demonstrate the results for human detection, human tracking, and human activity recognition.

Human activity recognition from UAV-captured video sequences

2020 ◽  
Vol 100 ◽  
pp. 107140 ◽  
Author(s):  
Hazar Mliki ◽  
Fatma Bouhlel ◽  
Mohamed Hammami
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Activity Recognition ◽  
Video Sequences

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 Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow

2021 ◽  
pp. 2250009
Author(s):  
Arati Kushwaha ◽  
Ashish Khare ◽  
Manish Khare
Keyword(s):  
Optical Flow ◽  
Activity Recognition ◽  
Human Activity ◽  
State Of The Art ◽  
Human Activity Recognition ◽  
Experimental Result ◽  
Video Sequences ◽  
Histogram Of Oriented Gradients ◽  
Art Methods ◽  
Orientation Information

Human activity recognition from video sequences has emerged recently as pivotal research area due to its importance in a large number of applications such as real-time surveillance monitoring, healthcare, smart homes, security, behavior analysis, and many more. However, lots of challenges also exist such as intra-class variations, object occlusion, varying illumination condition, complex background, camera motion, etc. In this work, we introduce a novel feature descriptor based on the integration of magnitude and orientation information of optical flow and histogram of oriented gradients which gives an efficient and robust feature vector for the recognition of human activities for real-world environment. In the proposed approach first we computed magnitude and orientation of the optical flow separately then a local-oriented histogram of magnitude and orientation of motion flow vectors are computed using histogram of oriented gradients followed by linear combination feature fusion strategy. The resultant features are then processed by a multiclass Support Vector Machine (SVM) classifier for activity recognition. The experimental results are performed over different publically available benchmark video datasets such as UT interaction, CASIA, and HMDB51 datasets. The effectiveness of the proposed approach is evaluated in terms of six different performance parameters such as accuracy, precision, recall, specificity, [Formula: see text]-measure, and Matthew’s correlation coefficient (MCC). To show the significance of the proposed method, it is compared with the other state-of-the-art methods. The experimental result shows that the proposed method performs well in comparison to other state-of-the-art methods.

scholarly journals Human Activity Recognition from the Acceleration Data of a Wearable Device. Which Features Are More Relevant by Activities?

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 1242 ◽  
Author(s):  
Macarena Espinilla ◽  
Javier Medina ◽  
Alberto Salguero ◽  
Naomi Irvine ◽  
Mark Donnelly ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Human Activity Recognition ◽  
Classification Algorithm ◽  
Wearable Device ◽  
K Nearest Neighbor ◽  
Multiple Features ◽  
Acceleration Data

Data driven approaches for human activity recognition learn from pre-existent large-scale datasets to generate a classification algorithm that can recognize target activities. Typically, several activities are represented within such datasets, characterized by multiple features that are computed from sensor devices. Often, some features are found to be more relevant to particular activities, which can lead to the classification algorithm providing less accuracy in detecting the activity where such features are not so relevant. This work presents an experimentation for human activity recognition with features derived from the acceleration data of a wearable device. Specifically, this work analyzes which features are most relevant for each activity and furthermore investigates which classifier provides the best accuracy with those features. The results obtained indicate that the best classifier is the k-nearest neighbor and furthermore, confirms that there do exist redundant features that generally introduce noise into the classification, leading to decreased accuracy.

Towards Intelligent Human Behavior Detection for Video Surveillance

2019 ◽  
pp. 884-917 ◽  
Author(s):  
Swati Nigam ◽  
Rajiv Singh ◽  
A. K. Misra
Keyword(s):  
Activity Recognition ◽  
Human Activity ◽  
Human Behavior ◽  
Human Activity Recognition ◽  
Human Detection ◽  
Video Sequences ◽  
Human Tracking ◽  
Feature Descriptor ◽  
Behavior Detection ◽  
Human Behavior Analysis

Computer vision techniques are capable of detecting human behavior from video sequences. Several state-of-the-art techniques have been proposed for human behavior detection and analysis. However, a collective framework is always required for intelligent human behavior analysis. Therefore, in this chapter, the authors provide a comprehensive understanding towards human behavior detection approaches. The framework of this chapter is based on human detection, human tracking, and human activity recognition, as these are the basic steps of human behavior detection process. The authors provide a detailed discussion over the human behavior detection framework and discuss the feature-descriptor-based approach. Furthermore, they have provided qualitative and quantitative analysis for the detection framework and demonstrate the results for human detection, human tracking, and human activity recognition.

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