Violence detection is very important for public safety.However,violence detection is not an easy task.Because recognizing violence in surveillance video requires not only spatial information but also sufficient tempor...Violence detection is very important for public safety.However,violence detection is not an easy task.Because recognizing violence in surveillance video requires not only spatial information but also sufficient temporal information.In order to highlight the time information,we propose an efficient deep learning architecture for violence detection based on temporal attention mechanism,which utilizes pre-trained MobileNetV3,convolutional LSTM and temporal attention block Temporal Adaptive(TA).TA block can focus on further refining temporal information from spatial information extracted from backbone.Experimental results show the proposed model is validated on three publicly datasets:Hockey Fight,Movies,and RWF-2000 datasets.展开更多
Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban t...Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban traffic travel pressure and reduce the frequency of accidents. Due to the randomness and fast changing speed of urban dynamic traffic data flow, most of the existing prediction methods lack the ability to model the dynamic temporal and spatial correlation of traffic data, so they cannot produce satisfactory prediction results. A spatio-temporal convolution network (ST-CNN) is proposed to solve the traffic flow prediction problem. The model consists of two parts: 1) a convolution block used to extract spatial features;2) a block of time used to characterize time. Data has been fully mined through two modules to output the prediction results of spatio-temporal characteristics, and at the same time, skip connection (direct connection) has been made between the two modules to avoid the problem of gradient explosion. The experimental results on two data sets show that ST-CNN is better than the baseline model.展开更多
The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, ...The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics. Data from different speakers of different dialects is usually used in training machine learning algorithms. Based on this data, an algorithm is created to accurately identify the speaker’s dialect. Arabic dialects can be detected and classified using several models and techniques available in literature. Various models have been proposed from different perspectives. Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects. The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques. Each stage of the proposed model has several techniques that can be used to detect and classify AD. The accuracy and capability of the proposed model will be performed in the future work.展开更多
文摘Violence detection is very important for public safety.However,violence detection is not an easy task.Because recognizing violence in surveillance video requires not only spatial information but also sufficient temporal information.In order to highlight the time information,we propose an efficient deep learning architecture for violence detection based on temporal attention mechanism,which utilizes pre-trained MobileNetV3,convolutional LSTM and temporal attention block Temporal Adaptive(TA).TA block can focus on further refining temporal information from spatial information extracted from backbone.Experimental results show the proposed model is validated on three publicly datasets:Hockey Fight,Movies,and RWF-2000 datasets.
文摘Urban traffic flow prediction plays an important role in traffic flow control and urban safety risk prevention and control. Timely and accurate traffic flow prediction can provide guidance for traffic, relieve urban traffic travel pressure and reduce the frequency of accidents. Due to the randomness and fast changing speed of urban dynamic traffic data flow, most of the existing prediction methods lack the ability to model the dynamic temporal and spatial correlation of traffic data, so they cannot produce satisfactory prediction results. A spatio-temporal convolution network (ST-CNN) is proposed to solve the traffic flow prediction problem. The model consists of two parts: 1) a convolution block used to extract spatial features;2) a block of time used to characterize time. Data has been fully mined through two modules to output the prediction results of spatio-temporal characteristics, and at the same time, skip connection (direct connection) has been made between the two modules to avoid the problem of gradient explosion. The experimental results on two data sets show that ST-CNN is better than the baseline model.
文摘The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker’s dialect. Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics. Data from different speakers of different dialects is usually used in training machine learning algorithms. Based on this data, an algorithm is created to accurately identify the speaker’s dialect. Arabic dialects can be detected and classified using several models and techniques available in literature. Various models have been proposed from different perspectives. Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects. The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques. Each stage of the proposed model has several techniques that can be used to detect and classify AD. The accuracy and capability of the proposed model will be performed in the future work.
