# Abnormal Behavior Detection in Uncrowded Videos with Two-Stream 3D Convolutional Neural Networks
## 5 Experimental Works
### Comparison with the State-of-the-Art  
To further evaluate the proposed approach, it was compared with state-of-the-art works reported in the literature for the detection of falling, loitering, and violence. To make a fair comparison, the study selected the existing approaches that work on RGB data (instead of other inputs such as depth maps). So far as the comparison criteria are concerned, this work has not been compared with a common baseline as there is no existing work in the literature addressing all three abnormal behaviors within the same study. This means that the proposed approach must be compared with other works that were trained on one or more datasets corresponding to only one behavior and hence evaluated on those specific datasets. On the other hand, since one of the main drivers of the idea of this work was generality, that is, to enable detection of three behaviors in different scenarios, the experiments in the current work were conducted with the system trained on a combined dataset. However, despite this difference in training and evaluation conditions, the dataset employed in this work is essentially a combination of the same datasets that were used in the other works. Therefore, it is expected that the comparison should still give a reasonable picture of the performance level of our model. So, instead of only presenting the results of metrics, this section will also provide brief details of the datasets and training conditions of each work. Furthermore, it should be noted that since researchers have used diverse performance measures, the results will be presented based on the metrics originally employed in each paper. A majority of the approaches have reported results of accuracy. However, as previously stated, since higher accuracy does not always imply a higher predictive power, precision and recall values of our method are also reported for all classes. The results of comparison with approaches for falling, loitering, and violence detection are tabulated in Tables 8-10, respectively. Our comparison includes both handcrafted based as well as deep learning approaches. Note that all methods included in this section used a binary classification-based approach for a single abnormal behavior detection, that is, to detect the presence of one of the fall, loitering, or violent behaviors in videos. [PERSON] and [PERSON] [65] use curvelet transforms and area ratios features to characterize the human body. They use an SVM classifier to identify the posture and then to distinguish fall events from other activities, they apply a Hidden Markov Model (HMM). They reported an accuracy of 97% on URF-DS. [PERSON] et al. [48] develop an augmented set of features named HLC (a combination of Histograms of Oriented Gradients (HOG), Local Binary Pattern (LBP), and feature extracted by the Deep Learning Framework Caffe) to represent human motion. Then by using two SVMs to classify the fall events, they have achieved a recall value of 93% on Multicam-DS. [PERSON] et al. [12] have combined the use of geometric features and CNNs. Specifically, they use Gaussian mixture model (GMM) to represent the features of each pixel pertaining to the foreground and proposed a novel method of head segmentation (locating, tracking, ellipse fitting of head). Later, a 2D-CNN is used to learn the correlation of the head and torso ellipses during the fall. For training and testing, they used a self-collected dataset of 102 videos containing 30 falls and 28 other activities and achieved an accuracy of 90% on this dataset. Our method is closely related to the work of [PERSON] et al. [8] where instead of using any feature engineering, a CNN model pretrained in Imagenet and UCF-101 has been used to detect falls based on RGB data and optical flow images. However, instead of using two parallel streams for RGB and optical flow, they train the model first on RGB and then on optical flow. They have reported results obtained