# Cascaded Vehicle Matching and Short-Term Spatial-Temporal Network for Smoky Vehicle Detection
## 2 Related Work
### Visual Smoke Detection  
Visual smoke detection aims to detect fire by recognizing early smoke or fire smoke anywhere within the field of the camera at a distance. In the past two decades, video-based wildfire smoke detection has been extensively studied. Here, we roughly categorize the video-based smoke detection methods into shallow feature-based methods and deep learning-based methods.  
Shallow feature based methods. These methods mainly extract structural and statistical features from visual signatures such as the motion, color, edge, obscurity, geometry, texture, and energy of smoke regions. [PERSON] and [PERSON] [38] implemented a real-time automatic smoke detection system for forest surveillance stations based on the assumption that the energy of the velocity distribution of a smoke plume is higher than that of other natural occurrences. [PERSON] et al. [39] used an optical flow and wavelet decomposition algorithm for wildfire smoke detection and monitoring. [PERSON] et al. [14] presented a close range (in 100 m) smoke detection system, which is mainly comprised of moving region detection and spatial-temporal wavelet transform. [PERSON] [16] proposed an accumulative motion orientation feature-based method for video smoke detection. [PERSON] et al. [18] applied volume local binary patterns for dynamic texture modelling and smoke detection. [PERSON] et al. [20] considered the scale invariance of local binary patterns and utilized Hamming distances for local binary patterns to address image-based smoke recognition. [PERSON] et al. [23] leveraged multi-layer Gabor filters for image-based smoke recognition. Overall, all these shallow feature-based methods focused on improving the robustness of hand-crafted texture or motion features, which may be limited in their representation abilities.  
Deep learning based methods. With the development of deep learning [26], convolutional neural networks (CNNs) have recently been extensively used for visual image classification and object detection [34; 40]. Visual smoke detection can be viewed as an object classification or detection task. From the characteristic of deep networks, we can roughly divide these deep networks into 2D appearance networks and spatial-temporal networks.  
For 2D networks, [PERSON] et al. [41] built their CNNs very similarly to the well-known LeNet-5 [42] with an increased number of feature maps in convolution layers, and used real smoke scenarios and achieved 97.9% accuracy, which is higher than the performance of traditional machine learning approaches. [PERSON] et al. [43] applied the well-known AlexNet [26] for visual smoke detection and achieved a high performance on open their smoke image dataset. [PERSON] et al. provided a comparative study of modern CNN models. To address image-based smoke detection, [PERSON] et al. [30] designed a new deep dual-channel neural network for high-level texture and contour information extraction.  
For spatial-temporal networks, [PERSON] et al. [29] designed a 3D CNN to extract the spatial-temporal features of smoke proposals. [PERSON] et al. [9] built a professional spatial-temporal model comprised of three CNNs on different orthogonal planes. [PERSON] et al. [28] proposed a 3D parallel fully convolutional network for real-time video-based smoke detection. In the other direction, recurrent neural networks have also been applied for video-based smoke detection [31].  
Overall, almost all the outlined methods focus on general wildfire smoke recognition or detection.