Smart cities are being developed worldwide with the use of technology to improve the quality of life of citizens and enhance their safety. Video surveillance is a key component of smart city infrastructure, as it involves the installation of cameras at strategic locations throughout the city for monitoring public spaces and providing real-time surveillance footage to law enforcement and other city representatives. Video surveillance systems have evolved rapidly in recent years, and are now integrated with advanced technologies like deep learning, blockchain, edge computing, and cloud computing. This study provides a comprehensive overview of video surveillance systems in smart cities, as well as the functions and challenges of those systems. The aim of this paper is to highlight the importance of video surveillance systems in smart cities and to provide insights into how they could be used to enhance safety, security, and the overall quality of life for citizens.

A Survey of Video Surveillance Systems in Smart City

As a unique physiological electrical signal in the human body, surface electromyography (sEMG) signals always include human movement intention and muscle state. Through the collection of sEMG signals, different gestures can be effectively recognized. At present, the convolutional neural network (CNN) has been widely applied to different gesture recognition systems. However, due to its inherent limitations in global context feature extraction, it exists a certain shortcoming on high-precision prediction tasks. To solve this issue, a CNN-transformer hybrid recognition approach is proposed for high-precision dynamic gesture prediction. In addition, the continuous wavelet transform (CWT) is proposed for to acquire the time-frequency maps. To realize effective feature representation of local features from the time-frequency maps, an attention fusion block (AFB) is proposed to build the deep CNN network branch to effectively extract key channel information and spatial information from local features. Faced with the inherent limitations in global context feature extraction of CNNs, a transformer network branch is proposed to model the global relationship between pixels, called convolution and transformer (CAT) network branch. In addition, a multiscale feature attention (MFA) block is proposed for effective feature aggregation of local features and global contexts by learning adaptive multiscale features and suppressing irrelevant scale information. The experimental results on the established multichannel sEMG signal time-frequency map dataset show that the proposed CNN transformer hybrid recognition network has competitive recognition performance compared with other state-of-the-art recognition networks, and the average recognition speed of each spectrogram on the test set is only 14.7 ms. The proposed network can effectively improve network performance and identification efficiency.

A CNN-Transformer Hybrid Recognition Approach for sEMG-Based Dynamic Gesture Prediction

As a unique physiological electrical signal in the human body, surface electromyography (sEMG) signals always include human movement intention and muscle state. Through the collection of sEMG signals, different gestures can be effectively recognized. At present, the convolutional neural network (CNN) has been widely applied to different gesture recognition systems. However, due to its inherent limitations in global context feature extraction, it exists a certain shortcoming on high-precision prediction tasks. To solve this issue, a CNN-transformer hybrid recognition approach is proposed for high-precision dynamic gesture prediction. In addition, the continuous wavelet transform (CWT) is proposed for to acquire the time-frequency maps. To realize effective feature representation of local features from the time-frequency maps, an attention fusion block (AFB) is proposed to build the deep CNN network branch to effectively extract key channel information and spatial information from local features. Faced with the inherent limitations in global context feature extraction of CNNs, a transformer network branch is proposed to model the global relationship between pixels, called convolution and transformer (CAT) network branch. In addition, a multi-scale feature attention block (MFA) is proposed for effective feature aggregation of local features and global contexts by learning adaptive multi-scale features and suppressing irrelevant scale information. The experimental results on the established multi-channel sEMG signal time-frequency map dataset show that the proposed CNN transformer hybrid recognition network has competitive recognition performance compared with other state-of-the-art recognition networks, and the average recognition speed of each spectrogram on the test set is only 14.7ms. The proposed network can effectively improve network performance and identification efficiency. 

A CNN-Transformer Hybrid Recognition Approach for sEMG-based Dynamic Gesture Prediction

Regarding increasingly complex scenarios in hand gesture recognition (HGR), it is challenging to implement a reliable HGR due to the non-adaptability of individual sensors to the environment and the discrepancy of personal habits. Multisensor fusion has been deemed an effective way to overcome the limitations of a single sensor. However, there is a lack of research on HGR to effectively establish bridges linking multimodal heterogeneous information. To address this issue, we propose a novel multimodal dynamic HGR method based on a two-branch fusion deformable network with Gram matching. First, a time-synchronized method is designed to preprocess the multimodal data. Second, a two-branch network is proposed to implement gesture classification based on radar-vision fusion. The input convolution is replaced by the deformable convolution to improve the generalization of gesture motion modeling. The long short-term memory (LSTM) unit is utilized to extract the temporal features of dynamic hand gestures. Third, Gram matching is presented as a loss function to mine high-dimensional heterogeneous information and maintain the integrity of radar-vision fusion. The experimental results indicate that the proposed method effectively improves the adaptability of the classifier to complex environments and exhibits satisfactory robustness to multiple subjects. Furthermore, ablation analysis shows that deformable convolution and Gram loss not only provide reliable gesture recognition but also enhance the generalization ability of the proposed methods in different field-of-view scenarios. 

A Multimodal Dynamic Hand Gesture Recognition Based on Radar–Vision Fusion

https://doi.org/10.1016/j.dib.2022.107977

Low resolution thermal imaging dataset of sign language digits

Hand gestures are a well-known and intuitive method of human-computer interaction. The majority of the research has concentrated on hand gesture recognition from the RGB images, however, little work has been done on recognition from videos. In addition, RGB cameras are not robust in varying lighting conditions. Motivated by this, we present the video based hand gestures recognition using the depth camera and a light weight convolutional neural network (CNN) model. We constructed a dataset and then used a light weight CNN model to detect and classify hand movements efficiently. We also examined the classification accuracy with a limited number of frames in a video gesture. We compare the depth camera’s video gesture recognition performance to that of the RGB camera. We evaluate the proposed model’s performance on edge computing devices and compare to benchmark models in terms of accuracy and inference time. The proposed model results in an accuracy of 99.48% on the RGB version of test dataset and 99.18% on the depth version of test dataset. Finally, we compare the accuracy of the proposed light weight CNN model with the state-of-the hand gesture classification models.

Video Hand Gestures Recognition Using Depth Camera and Lightweight CNN

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Video Hand Gestures Recognition Using Depth Camera and Lightweight CNN