Human action recognition in crowded surveillance video sequences by using features taken from key-point trajectories

2011 ◽  
Author(s):  
Masaki Takahashi ◽  
Masahide Naemura ◽  
Mahito Fujii ◽  
Shin'ichi Satoh
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Sequences ◽  
Surveillance Video ◽  
Point Trajectories

scholarly journals Hierarchical dynamic depth projected difference images–based action recognition in videos with convolutional neural networks

2019 ◽  
Vol 16 (1) ◽  
pp. 172988141882509 ◽  
Author(s):  
Hanbo Wu ◽  
Xin Ma ◽  
Yibin Li
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Temporal Information ◽  
Superior Performance ◽  
Video Sequences ◽  
Depth Video ◽  
Difference Images

Temporal information plays a significant role in video-based human action recognition. How to effectively extract the spatial–temporal characteristics of actions in videos has always been a challenging problem. Most existing methods acquire spatial and temporal cues in videos individually. In this article, we propose a new effective representation for depth video sequences, called hierarchical dynamic depth projected difference images that can aggregate the action spatial and temporal information simultaneously at different temporal scales. We firstly project depth video sequences onto three orthogonal Cartesian views to capture the 3D shape and motion information of human actions. Hierarchical dynamic depth projected difference images are constructed with the rank pooling in each projected view to hierarchically encode the spatial–temporal motion dynamics in depth videos. Convolutional neural networks can automatically learn discriminative features from images and have been extended to video classification because of their superior performance. To verify the effectiveness of hierarchical dynamic depth projected difference images representation, we construct a hierarchical dynamic depth projected difference images–based action recognition framework where hierarchical dynamic depth projected difference images in three views are fed into three identical pretrained convolutional neural networks independently for finely retuning. We design three classification schemes in the framework and different schemes utilize different convolutional neural network layers to compare their effects on action recognition. Three views are combined to describe the actions more comprehensively in each classification scheme. The proposed framework is evaluated on three challenging public human action data sets. Experiments indicate that our method has better performance and can provide discriminative spatial–temporal information for human action recognition in depth videos.

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.

Discriminative Action Recognition Using Supervised Latent Topic Model

2012 ◽  
Vol 190-191 ◽  
pp. 1125-1128
Author(s):  
Huan Xin Zou ◽  
Hao Sun ◽  
Ke Feng Ji
Keyword(s):  
Action Recognition ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Sequences ◽  
Discriminative Learning ◽  
Action Categorization ◽  
Latent Topic ◽  
Topic Structure

We present a discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure which is most relevant to action categorization. We test our algorithm on two challenging datasets. Experimental results demonstrate the effectiveness of our algorithm.

scholarly journals Semi-Supervised Image-to-Video Adaptation for Video Action Recognition

2021 ◽  
Vol 9 (VIII) ◽  
pp. 760-763
Author(s):  
Rohan Munshi
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Action ◽  
Camera Motion ◽  
Video Adaptation ◽  
Surveillance Video ◽  
Systematic Classification ◽  
The Public ◽  
Background Clutter ◽  
Public Datasets

Given a sequence of images i.e. video, the task given a sequence of images i.e. video, the task of action recognition is to identify the most same action among the action sequences learned by the system. Such human action recognition is based on evidence gathered from videos. It has a lot of applications including surveillance, video indexing, biometrics, telehealth, and human-computer interaction. Vision-based human activity recognition is plagued by numerous challenges thanks to reading changes, occlusion, variation in execution rate, camera motion, and background clutter. In this survey, we provide an overview and report of the existing methods based on their ability to handle these challenges as well as how these methods can be generalized and their ability to detect abnormal actions. Such systematic classification can facilitate researchers to spot the acceptable ways on the market to deal with every one of the challenges visaged and their limitations. In addition to this, we also identify the public datasets and the challenges posed by them. From this survey, we have a tendency to draw conclusions relating to however well a challenge has been resolved, and that we determine potential analysis areas that need more work.

Human Action Recognition Based on a Spatio-Temporal Video Autoencoder

2020 ◽  
Vol 34 (11) ◽  
pp. 2040001
Author(s):  
Anderson Carlos Sousa e Santos ◽  
Helio Pedrini
Keyword(s):  
Action Recognition ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Sequences ◽  
Computer Vision Applications ◽  
Recognition Systems ◽  
Spatio Temporal ◽  
Human Operators ◽  
Accuracy Rates

Due to rapid advances in the development of surveillance cameras with high sampling rates, low cost, small size and high resolution, video-based action recognition systems have become more commonly used in various computer vision applications. Human operators can be supported with the aid of such systems to detect events of interest in video sequences, improving recognition results and reducing failure cases. In this work, we propose and evaluate a method to learn two-dimensional (2D) representations from video sequences based on an autoencoder framework. Spatial and temporal information is explored through a multi-stream convolutional neural network in the context of human action recognition. Experimental results on the challenging UCF101 and HMDB51 datasets demonstrate that our representation is capable of achieving competitive accuracy rates when compared to other approaches available in the literature.

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