# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 2 Related Work
### Deep Learning-Based Methods  
In a broader context, the recent studies in the human action recognition and classification domain extend more classical architectures, such as [27; 35; 36], to further improve performance. In [21], the authors propose a method to use pre-trained 2D CNNs for both spatial and temporal streams in action classification problems. To do so, instead of using an optical stream, they adopt a pre-trained 2D CNN for the temporal stream. They form a Stacked Grayscale 3-channel Image (SG3I) by selecting three RGB frames at arbitrary points in the video. SG3 Is are then used to fine-tune the pre-trained 2D CNN. [37] proposes a Combined Video-stream Deep Network that extracts the spatio-temporal features from video clips using a ResNet. They apply ResNets models pre-trained in Kinetics dataset to UCF-101 and extract video stream features. Next, similar to our approach, they use optical flow graphs of the UCF-101 dataset as input to optical stream and extract the optical features therein. Finally, features from both streams are combined. A different approach to human action recognition is proposed in [38]. This study is centered around the distance transform and entropy features extracted from images of human silhouettes obtained after background subtraction. As these features provide the shape and local variation information, they are input to deep networks to recognize human actions. Another related line of research addresses anomaly detection in video surveillance. In [39], a method has been proposed for surveillance based on Unmanned Aerial Vehicles (UAVs). To extract features from UAV videos, they utilized a 3-way process consisting of the use of a CNN, Histogram of Oriented Gradient (HOG), and HOG3D. Finally, a one-class support vector machine has been applied for classification. Recently, [PERSON] et al. [40] proposed the use of a particular scheme of deep learning using a fully convolutional neural network (FCN) as a regressor for crowd counting applied to aerial scenes shot from UAVs. They train two FCNs simultaneously on the captured images of the crowd as well as the corresponding crowd heatmaps. Similarly, in [41], taking into account the limited computational capabilities of UAVs, a lightweight crowd detection method was proposed to ensure the safe landing of UAVs. An essentially different solution has been proposed in [42]. This study exploits the one-shot learning strategy for anomaly recognition to develop a method for one-shot anomaly detection for surveillance systems. Their method is based on a lightweight siamese 3D CNN that efficiently determines the similarity between two anomaly sequences. In [6], the spatio-temporal features are extracted by utilizing a pre-trained ResNet-50 architecture. Then, the extracted features are passed to a multi-layer Bi-directional Long Short-term Memory (BDLSTM) model to classify anomalies in surveillance videos. [PERSON] et al. [30] proposed a method to detect five different behaviors (of which two were considered abnormal) in a specific industrial setting. They used DenseNet in a two-stream architecture to extract spatial and temporal features and applied a particular technique to alleviate the problems resulting from imbalanced data. The model was trained and tested on a self-constructed dataset. Ref. [43] used an ImageNet pretrained VGG16 architecture and InceptionV3 to finetune on UCF-Crime dataset [44]. In the specific context of the current study, some approaches aim at detecting specific abnormal behaviors. [PERSON] et al. [8] have proposed an approach for fall detection that uses CNN to learn the detection of falls from optical flow images. They train the VGG-16 network on ImageNet and optical stacks of UCF-101 [45]. Finally, they apply transfer learning and fine-tune the network on fall datasets. This approach is similar to ours, but our 3D architecture allows more effective video processing. In contrast with [PERSON] et al. 's work, instead of using the modified VGG-16 architecture, [PERSON] et al.