Action Recognition Based on Sequential 2D-CNN for Surveillance Systems

2019 ◽

Vol 09 ◽

Author(s):

Rajat Khurana ◽

Alok Kumar Singh Kushwaha

Keyword(s):

Action Recognition ◽

Depth Map ◽

Good Choice ◽

Surveillance Systems ◽

Activity Detection ◽

Learning Capability ◽

Proposed Model ◽

Care Activity ◽

Increasing Demand ◽

Depth Motion Maps

Background & Objective: Identification of human actions from video has gathered much attention in past few years. Most of the computer vision tasks such as Health Care Activity Detection, Suspicious Activity detection, Human Computer Interactions etc. are based on the principle of activity detection. Automatic labelling of activity from videos frames is known as activity detection. Motivation of this work is to use most out of the data generated from sensors and use them for recognition of classes. Recognition of actions from videos sequences is a growing field with the upcoming trends of deep neural networks. Automatic learning capability of Convolutional Neural Network (CNN) make them good choice as compared to traditional handcrafted based approaches. With the increasing demand of RGB-D sensors combination of RGB and depth data is in great demand. This work comprises of the use of dynamic images generated from RGB combined with depth map for action recognition purpose. We have experimented our approach on pre trained VGG-F model using MSR Daily activity dataset and UTD MHAD Dataset. We achieve state of the art results. To support our research, we have calculated different parameters apart from accuracy such as precision, F score, recall. Conclusion: Accordingly, the investigation confirms improvement in term of accuracy, precision, F-Score and Recall. The proposed model is 4 Stream model is prone to occlusion, used in real time and also the data from the RGB-D sensor is fully utilized.

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Human Action Recognition Based on Bag-of-Words

Iraqi Journal of Science ◽

10.24996/ijs.2020.61.5.27 ◽

2020 ◽

pp. 1202-1214

Author(s):

Riyadh Sahib Abdul Ameer ◽

Mohammed Al-Taei

Keyword(s):

Action Recognition ◽

False Positive Rate ◽

Human Action Recognition ◽

True Positive Rate ◽

Human Action ◽

Misclassification Rate ◽

Surveillance Systems ◽

Bag Of Words ◽

Camera Motion ◽

Positive Rate

Human action recognition has gained popularity because of its wide applicability, such as in patient monitoring systems, surveillance systems, and a wide diversity of systems that contain interactions between people and electrical devices, including human computer interfaces. The proposed method includes sequential stages of object segmentation, feature extraction, action detection and then action recognition. Effective results of human actions using different features of unconstrained videos was a challenging task due to camera motion, cluttered background, occlusions, complexity of human movements, and variety of same actions performed by distinct subjects. Thus, the proposed method overcomes such problems by using the fusion of features concept for the development of a powerful human action descriptor. This descriptor is modified to create a visual word vocabulary (or codebook) which yields a Bag-of-Words representation. The True Positive Rate (TPR) and False Positive Rate (FPR) measures gave a true indication about the proposed HAR system. The computed Accuracy (Ar) and the Error (misclassification) Rate (Er) reveal the effectiveness of the system with the used dataset.

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Multiview Layer Fusion Model for Action Recognition Using RGBD Images

Computational Intelligence and Neuroscience ◽

10.1155/2018/9032945 ◽

2018 ◽

Vol 2018 ◽

pp. 1-22

Author(s):

Pongsagorn Chalearnnetkul ◽

Nikom Suvonvorn

Keyword(s):

Real Time ◽

Action Recognition ◽

Low Complexity ◽

Feature Model ◽

Surveillance Systems ◽

Fusion Model ◽

Average Precision ◽

Healthcare Applications ◽

Depth Images ◽

Novel Method

Vision-based action recognition encounters different challenges in practice, including recognition of the subject from any viewpoint, processing of data in real time, and offering privacy in a real-world setting. Even recognizing profile-based human actions, a subset of vision-based action recognition, is a considerable challenge in computer vision which forms the basis for an understanding of complex actions, activities, and behaviors, especially in healthcare applications and video surveillance systems. Accordingly, we introduce a novel method to construct a layer feature model for a profile-based solution that allows the fusion of features for multiview depth images. This model enables recognition from several viewpoints with low complexity at a real-time running speed of 63 fps for four profile-based actions: standing/walking, sitting, stooping, and lying. The experiment using the Northwestern-UCLA 3D dataset resulted in an average precision of 86.40%. With the i3DPost dataset, the experiment achieved an average precision of 93.00%. With the PSU multiview profile-based action dataset, a new dataset for multiple viewpoints which provides profile-based action RGBD images built by our group, we achieved an average precision of 99.31%.

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Spatio-Temporal Analysis for Human Action Detection and Recognition in Uncontrolled Environments

International Journal of Multimedia Data Engineering and Management ◽

10.4018/ijmdem.2015010101 ◽

2015 ◽

Vol 6 (1) ◽

pp. 1-18 ◽

Cited By ~ 28

Author(s):

Dianting Liu ◽

Yilin Yan ◽

Mei-Ling Shyu ◽

Guiru Zhao ◽

Min Chen

Keyword(s):

Action Recognition ◽

Hamming Distance ◽

Gaussian Mixture ◽

Human Action ◽

Surveillance Systems ◽

Camera Motion ◽

Action Detection ◽

Public Data ◽

Human Action Detection ◽

Detection And Recognition

Understanding semantic meaning of human actions captured in unconstrained environments has broad applications in fields ranging from patient monitoring, human-computer interaction, to surveillance systems. However, while great progresses have been achieved on automatic human action detection and recognition in videos that are captured in controlled/constrained environments, most existing approaches perform unsatisfactorily on videos with uncontrolled/unconstrained conditions (e.g., significant camera motion, background clutter, scaling, and light conditions). To address this issue, the authors propose a robust human action detection and recognition framework that works effectively on videos taken in controlled or uncontrolled environments. Specifically, the authors integrate the optical flow field and Harris3D corner detector to generate a new spatial-temporal information representation for each video sequence, from which the general Gaussian mixture model (GMM) is learned. All the mean vectors of the Gaussian components in the generated GMM model are concatenated to create the GMM supervector for video action recognition. They build a boosting classifier based on a set of sparse representation classifiers and hamming distance classifiers to improve the accuracy of action recognition. The experimental results on two broadly used public data sets, KTH and UCF YouTube Action, show that the proposed framework outperforms the other state-of-the-art approaches on both action detection and recognition.

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