scholarly journals Spatial–Temporal Attention Two-Stream Convolution Neural Network for Smoke Region Detection

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 66
Author(s):  
Zhipeng Ding ◽  
Yaqin Zhao ◽  
Ao Li ◽  
Zhaoxiang Zheng
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Fire Behavior ◽  
Flow Characteristics ◽  
Attention Mechanism ◽  
Detection Accuracy ◽  
Temporal Attention ◽  
Multiple Perspectives ◽  
Region Detection ◽  
Spatio Temporal

Smoke detection is of great significance for fire location and fire behavior analysis in a fire video surveillance system. Smoke image classification methods based on a deep convolution network have achieved high accuracy. However, the combustion of different types of fuel can produce smoke with different colors, such as black smoke, grey smoke, and white smoke. Additionally, the diffusion characteristic of smoke can lead to transparent smoke regions accompanied by colors and textures of background objects. Therefore, compared with smoke image classification, smoke region detection is a challenging task. This paper proposes a two-stream convolutional neural network based on spatio-temporal attention mechanism for smoke region segmentation (STCNNsmoke). The spatial stream extracts spatial features of foreground objects using the semi-supervised ranking model. The temporal stream uses optical flow characteristics to represent the dynamic characteristics of smoke such as diffusion and flutter features. Specifically, the spatio-temporal attention mechanism is presented to fuse the spatial and temporal characteristics of smoke and pay more attention to the moving regions with smoke colors and textures by predicting attention weights of channels. Furthermore, the spatio-temporal attention model improves the channel response of smoke-moving regions for the segmentation of complete smoke regions. The proposed method is evaluated and analyzed from multiple perspectives such as region detection accuracy and anti-interference. The experimental results showed that the proposed method significantly improved the ability of segmenting thin smoke and small smoke.

Multi-Head Spatio-Temporal Attention Mechanism for Urban Anomaly Event Prediction

2021 ◽  
Vol 5 (3) ◽  
pp. 1-21
Author(s):  
Huiqun Huang ◽  
Xi Yang ◽  
Suining He
Keyword(s):  
New York ◽  
Experimental Studies ◽  
Attention Mechanism ◽  
City Management ◽  
Temporal Attention ◽  
Spatial Correlations ◽  
Time Step ◽  
Event Prediction ◽  
Spatio Temporal ◽  
Prediction Approach

Timely forecasting the urban anomaly events in advance is of great importance to the city management and planning. However, anomaly event prediction is highly challenging due to the sparseness of data, geographic heterogeneity (e.g., complex spatial correlation, skewed spatial distribution of anomaly events and crowd flows), and the dynamic temporal dependencies. In this study, we propose M-STAP, a novel Multi-head Spatio-Temporal Attention Prediction approach to address the problem of multi-region urban anomaly event prediction. Specifically, M-STAP considers the problem from three main aspects: (1) extracting the spatial characteristics of the anomaly events in different regions, and the spatial correlations between anomaly events and crowd flows; (2) modeling the impacts of crowd flow dynamic of the most relevant regions in each time step on the anomaly events; and (3) employing attention mechanism to analyze the varying impacts of the historical anomaly events on the predicted data. We have conducted extensive experimental studies on the crowd flows and anomaly events data of New York City, Melbourne and Chicago. Our proposed model shows higher accuracy (41.91% improvement on average) in predicting multi-region anomaly events compared with the state-of-the-arts.

scholarly journals Research on Target Tracking Algorithm Based on Siamese Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Haibo Pang ◽  
Qi Xuan ◽  
Meiqin Xie ◽  
Chengming Liu ◽  
Zhanbo Li
Keyword(s):  
Neural Network ◽  
Target Tracking ◽  
Evaluation Criteria ◽  
Attention Mechanism ◽  
Tracking Algorithm ◽  
Temporal Attention ◽  
Tracking Accuracy ◽  
Current Frame ◽  
Tracking Process ◽  
Recognition Ability

Target tracking is a significant topic in the field of computer vision. In this paper, the target tracking algorithm based on deep Siamese network is studied. Aiming at the situation that the tracking process is not robust, such as drift or miss the target, the tracking accuracy and robustness of the algorithm are improved by improving the feature extraction part and online update part. This paper adds SE-block and temporal attention mechanism (TAM) to the framework of Siamese neural network. SE-block can refine and extract features; different channels are given different weights according to their importance which can improve the discrimination of the network and the recognition ability of the tracker. Temporal attention mechanism can update the target state by adjusting the weights of samples at current frame and historical frame to solve the model drift caused by the existence of similar background. We use cross-entropy loss to distinguish the targets in different sequences so that their distance in the feature domains is longer and the features are easier to identify. We train and test the network on three benchmarks and compare with several state-of-the-art tracking methods. The experimental results demonstrate that the algorithm proposed is superior to other methods in tracking effect diagram and evaluation criteria. The proposed algorithm can solve the occlusion problem effectively while ensuring the real-time performance in the process of tracking.

scholarly journals Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection

2020 ◽  
Vol 34 (01) ◽  
pp. 362-369 ◽  
Author(s):  
Dawei Cheng ◽  
Sheng Xiang ◽  
Chencheng Shang ◽  
Yiyi Zhang ◽  
Fangzhou Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Credit Card ◽  
Domain Knowledge ◽  
Empirical Studies ◽  
Fraud Detection ◽  
Temporal Attention ◽  
Credit Card Fraud ◽  
Domain Experts ◽  
Spatio Temporal ◽  
Better Than

Credit card fraud is an important issue and incurs a considerable cost for both cardholders and issuing institutions. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records. But manually generating features needs domain knowledge and may lay behind the modus operandi of fraud, which means we need to automatically focus on the most relevant patterns in fraudulent behavior. Therefore, in this work, we propose a spatial-temporal attention-based neural network (STAN) for fraud detection. In particular, transaction records are modeled by attention and 3D convolution mechanisms by integrating the corresponding information, including spatial and temporal behaviors. Attentional weights are jointly learned in an end-to-end manner with 3D convolution and detection networks. Afterward, we conduct extensive experiments on real-word fraud transaction dataset, the result shows that STAN performs better than other state-of-the-art baselines in both AUC and precision-recall curves. Moreover, we conduct empirical studies with domain experts on the proposed method for fraud post-analysis; the result demonstrates the effectiveness of our proposed method in both detecting suspicious transactions and mining fraud patterns.

scholarly journals Attention graph: Learning effective visual features for large-scale image classification

2022 ◽  
Vol 16 ◽  
pp. 174830262110653
Author(s):  
Xuelian Cui ◽  
Zhanjie Zhang ◽  
Tao Zhang ◽  
Zhuoqun Yang ◽  
Jie Yang
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Spatial Attention ◽  
Network Model ◽  
Large Scale ◽  
Spatial Dimension ◽  
Attention Mechanism ◽  
Main Function ◽  
Proposed Model ◽  
Informative Part

In recent years, the research of deep learning has received extensive attention, and many breakthroughs have been made in various fields. On this basis, a neural network with the attention mechanism has become a research hotspot. In this paper, we try to solve the image classification task by implementing channel and spatial attention mechanism which improve the expression ability of neural network model. Different from previous studies, we propose an attention module consisting of channel attention module (CAM) and spatial attention module (SAM). The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. Besides, this module is lightweight and can be easily integrated into image classification algorithms. In the experiment, we combine the deep residual network model with the attention module and the experimental results show that the proposed method brings higher image classification accuracy. The channel attention module adds weight to the signals on different convolution channels to represent the correlation. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Through a large number of comparative experiments, our proposed model achieved outstanding performance.

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