“…The technique is a window-based descriptor used to compute points of interest, where the window is divided into an frequency grid of the histograms. The frequency histogram is generated from each grid cell to indicate the magnitude and direction of the edge for every individual cell [ 3 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Real-time skeleton estimation algorithms are used in commercial deep integrate cameras. This technology allows the fast and easy joints extraction of the human body [ 3 ]. Some studies, only use part of the body in a skeleton method, such as hands.…”
Automated human action recognition is one of the most attractive and practical research fields in computer vision. In such systems, the human action labelling is based on the appearance and patterns of the motions in the video sequences; however, majority of the existing research and most of the conventional methodologies and classic neural networks either neglect or are not able to use temporal information for action recognition prediction in a video sequence. On the other hand, the computational cost of a proper and accurate human action recognition is high. In this paper, we address the challenges of the preprocessing phase, by an automated selection of representative frames from the input sequences. We extract the key features of the representative frame rather than the entire features. We propose a hierarchical technique using background subtraction and HOG, followed by application of a deep neural network and skeletal modelling method. The combination of a CNN and the LSTM recursive network is considered for feature selection and maintaining the previous information; and finally, a Softmax-KNN classifier is used for labelling the human activities. We name our model as “Hierarchical Feature Reduction & Deep Learning”-based action recognition method, or HFR-DL in short. To evaluate the proposed method, we use the UCF101 dataset for the benchmarking which is widely used among researchers in the action recognition research field. The dataset includes 101 complicated activities in the wild. Experimental results show a significant improvement in terms of accuracy and speed in comparison with eight state-of-the-art methods.
“…The technique is a window-based descriptor used to compute points of interest, where the window is divided into an frequency grid of the histograms. The frequency histogram is generated from each grid cell to indicate the magnitude and direction of the edge for every individual cell [ 3 ]. Fig.…”
Section: Methodsmentioning
confidence: 99%
“…Real-time skeleton estimation algorithms are used in commercial deep integrate cameras. This technology allows the fast and easy joints extraction of the human body [ 3 ]. Some studies, only use part of the body in a skeleton method, such as hands.…”
Automated human action recognition is one of the most attractive and practical research fields in computer vision. In such systems, the human action labelling is based on the appearance and patterns of the motions in the video sequences; however, majority of the existing research and most of the conventional methodologies and classic neural networks either neglect or are not able to use temporal information for action recognition prediction in a video sequence. On the other hand, the computational cost of a proper and accurate human action recognition is high. In this paper, we address the challenges of the preprocessing phase, by an automated selection of representative frames from the input sequences. We extract the key features of the representative frame rather than the entire features. We propose a hierarchical technique using background subtraction and HOG, followed by application of a deep neural network and skeletal modelling method. The combination of a CNN and the LSTM recursive network is considered for feature selection and maintaining the previous information; and finally, a Softmax-KNN classifier is used for labelling the human activities. We name our model as “Hierarchical Feature Reduction & Deep Learning”-based action recognition method, or HFR-DL in short. To evaluate the proposed method, we use the UCF101 dataset for the benchmarking which is widely used among researchers in the action recognition research field. The dataset includes 101 complicated activities in the wild. Experimental results show a significant improvement in terms of accuracy and speed in comparison with eight state-of-the-art methods.
“…The visuals can only be seen after the theft happened in the ATM after several hours later from the incident. Machine learning algorithms and a Camera for detecting can monitor well, but the situations are not always ideal, and the Camera cannot predict the persons entering inside the ATM (Bajaj, et al 2019). As every individual is not educated, some people who are not aware of using the ATM may accompany people or ask the public to help.…”
“…This wide range enables 360°protection. This PIR, in addition to a Smart Camera, enables improved surveillance (Bajaj, et al 2019). Surveillance along with the security with artificial intelligence locking systems are implemented (Deshpande 2018).…”
Security and safety are a necessity for automated teller machines (ATM). The ATM security system is implemented using the Internet of things (IoT) and GPS (global positioning system). The main idea of this project is to develop an ATM surveillance and security system. In this project, when any physical attack against the ATM takes place, then information about the attack is sent using IoT and also alerts the surrounding area using a buzzer, at the same time the entire data from the sensors is sent to the developed mobile application and puts alert message to the bank officials. The officials who have control over the mobile application can control the Door through their mobile to lock from their location remotely. To prevent the escape of the thief chloroform connected to the controller through relay can also be sprayed inside the ATM by the officials remotely from their place using the mobile app. The Camera (ESP32) is used for live video coverage and to monitor the activity inside the ATM. The Camera will not only record the activity but also, transmit will live video taken inside the ATM and the ATM location as latitude and longitude are tracked using GPS. The system is connected to the Blynk mobile application. The sensor and GPS data are read by the microcontroller and these data are sent to the Blynk application. With the help of the Blynk application, the official who has access to it can control the relays and the respective devices connected to the relay to turn it ON or OFF. It can be used in many real-time applications such as banks, homes, and industries.
“…The technique is a window-based descriptor used to compute points of interest, where the window is divided into an n × n frequency grid of the histograms. The frequency histogram is generated from each grid cell to indicate the magnitude and direction of the edge for every individual cell [36]. The cells are interconnected and HOG calculates the derivative of each cell (or sub-image), I, with respect to X and Y as shown in Eq.…”
Section: B) Bgs and Human Action Identificationmentioning
Automated human action recognition is one of the most attractive and practical research fields in computer vision, in spite of its high computational costs. In such systems, the human action labelling is based on the appearance and patterns of the motions in the video sequences; however, the conventional methodologies and classic neural networks cannot use temporal information for action recognition prediction in the upcoming frames in a video sequence. On the other hand, the computational cost of the preprocessing stage is high. In this paper, we address challenges of the preprocessing phase, by an automated selection of representative frames among the input sequences. Furthermore, we extract the key features of the representative frame rather than the entire features. We propose a hybrid technique using background subtraction and HOG, followed by application of a deep neural network and skeletal modelling method. The combination of a CNN and the LSTM recursive network is considered for feature selection and maintaining the previous information, and finally, a Softmax-KNN classifier is used for labelling human activities. We name our model as "Feature Reduction \& Deep Learning" based action recognition method, or FR-DL in short. To evaluate the proposed method, we use the UCF dataset for the benchmarking which is widely-used among researchers in action recognition research. The dataset includes 101 complicated activities in the wild. Experimental results show a significant improvement in terms of accuracy and speed in comparison with six state-of-the-art articles.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
