Multi-view Multi-modal Approach Based on 5S-CNN and BiLSTM Using Skeleton, Depth and RGB Data for Human Activity Recognition

A review on deep learning applications with semantics

PurposeIn this study, supervision and control of the possible problems among people over a large area with a limited number of drone cameras and security staff is established.Design/methodology/approachThese drones, namely unmanned aerial vehicles (UAVs) will be adaptively and automatically distributed over the crowds to control and track the communities by the proposed system. Since crowds are mobile, the design of the drone clusters will be simultaneously re-organized according to densities and distributions of people. An adaptive and dynamic distribution and routing mechanism of UAV fleets for crowds is implemented to control a specific given region. The nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance.FindingsThe nine popular clustering algorithms have been used and tested in the presented mechanism to gain better performance. An outperformed clustering performance from the aggregated model has been received when compared with a singular clustering method over five different test cases about crowds of human distributions. This study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.Originality/valueThis study has three basic components. The first one is to divide the human crowds into clusters. The second one is to determine an optimum route of UAVs over clusters. The last one is to direct the most appropriate security personnel to the events that occurred.

Efficient path planning of drone swarms over clustered human crowds in social events

Huge amounts of data are generated with the emergence of new sensor technologies. Human activity recognition (HAR) datasets are generated from cameras, such as video or still images, capturing human behavior through sensors such as gyroscopes, Bluetooth, sound sensors, and accelerometers. These generated data sources are collected by the researchers and formed into open datasets. However, these datasets often show issues during dataset construction, sharing, and searching, which could produce further challenges for the reuse of the data by others. The main objective of this research is to explore the current issues and challenges faced by researchers in the HAR domain. A detail literature review was conducted to extract information from the published literature. Similarly, a questionnaire survey was sent to selected researchers having expertise in the HAR domain, who work with open datasets. The main issues and challenges were identified and classified into a hierarchical structure. This research will help HAR researchers to be aware of the current issues and challenges in the field of HAR open datasets. It will help to promote important attributes applicable to many open datasets, such as privacy, anonymity, platform maintenance, datasets’ descriptions, metadata, environmental conditions, resources, and training, while constructing and sharing new datasets.

Open Datasets in Human Activity Recognition Research—Issues and Challenges: A Review

Abstract. In the artificial intelligence domain, human action recognition (HAR) has evolved as one of the major active research topics as a reason of diverse applications, namely video surveillance. The extensive variation types amidst routine human activities make the recognition procedure much more intricate. As a reason for an increment in the cameras’ usage, automated systems are essential to categorize activities, such as these utilizing computationally intelligent methodologies, namely machine learning and deep learning (DL). This paper’s organization covers all the characteristics of the HAR’s general framework. This work talks about the public dataset’s characteristics utilized that are aimed at HAR. After that, it summarizes and classifies the recently published research enhancement under the common framework. The merits and demerits of dimensionality reduction, action representation, and as well as action analysis methodologies are offered. After that, the outcomes exhibit the DL methodologies’ performance via graphs. This article gives a clear vision of what we can explore in the activity recognition area. The work talks about HAR’s challenges and its future directions.

Survey on artificial intelligence-based human action recognition in video sequences

Improved image clustering with deep semantic embedding

The Iterative Closest Point (ICP) algorithm is one of the most important algorithms for ge-ometric alignment of three-dimensional surface registration, which is frequently used in computer vision tasks, including the Simultaneous Localization And Mapping (SLAM) tasks. In this paper, we illustrate the theoretical principles of the ICP algorithm, how it can be used in surface registration tasks, and the traditional taxonomy of the variants of the ICP algorithm. As SLAM is becoming a popular topic, we also introduce a SLAM-oriented taxonomy of the ICP algorithm, based on the characteristics of each type of SLAM task, including whether the SLAM task is online or not and whether the landmarks are present as features in the SLAM task. We make a synthesis of each type of SLAM task by compar-ing several up-to-date research papers and analyzing their implementation details.

pdf/icp-algorithm-theory-practice-and-its-slam-oriented-taxonomy-2mhusbpe.pdf

ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy

The invention relates to an image clustering algorithm based on depth semantic embedding, comprising the following steps of 1 dividing an image data set into a training set and a test set, and obtaining respective data feature spaces; 2 obtaining a mapping function of the image data from the data feature space of the training set obtained in the step 1 to the semantic space of the training set, and obtaining the semantic space of the test set through the mapping function; 3 taking the result obtained in the step 2 as an input layer, fusing and reducing dimensions through self-coding to obtaina low-dimensional embedded space with semantic information and original features; 4 clustering in the low-dimensional embedded space with semantic information and original features obtained in the step 4 by using the KL divergence function, and if the KL divergence function converges, ending the clustering; otherwise, returning to step 3 and updating the input layer of Step 3. The algorithm of theinvention effectively improves the discriminability of the data features and improves the clustering effect.

An image clustering algorithm based on depth semantic embedding

Human action recognition (HAR)in videos is a challenging task in computer vision. Conventional methods are prone to explore the spatiotemporal or optical representations for video actions. However, optical representation might be inefficient in some real-life situations, such as object occlusion and dim light. To address this issue, this paper presents a novel approach for human action recognition by jointly exploiting video and Wi-Fi clues. We leverage the fact that Wi-Fi signals carry discriminative information of human actions, which is robust to optical limitations. To validate this innovative thought, we conceive a practical framework for HAR and setup a dataset containing both video clips and Wi-Fi Channel State Information of human actions. The 3D convolutional neural network was used to extract the video features and the statistical algorithms were used to extract radio features. A classical linear support vector machine is employed as the classifier after the video and radio feature fusion. Comprehensive experiments on this dataset achieved desirable results with the maximum improvement in accuracy by 10%. This demonstrates our promising findings: with the aid of Wi-Fi Channel State Information, the performance of the video action recognition methods can be improved significantly, even under the optical limitation.

Multi modal human action recognition for video content matching

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Improved image clustering with deep semantic embedding

ICP Algorithm: Theory, Practice And Its SLAM-oriented Taxonomy

An image clustering algorithm based on depth semantic embedding

Multi modal human action recognition for video content matching