## 3 Related Works
Effective results were reported, for example, true positive rate 0.78 and false positive rate 0.19, improving 0.13 in comparison with methods that do not take into account the social context. These results suggest that inferring social connections between people helps improve decision making in the detection process. [PERSON] and [PERSON] [26] presented an abnormal behavior detection system based on hybrid agents for complex scenarios studying group interaction and individual behavior. In the proposed system, agents are categorized into static and dynamic. Static agents are located at fixed points, in order to compute temporal movement information (e.g., speed and direction) of the objects belonging to the background of the image, considering the optical flow variation. Dynamic agents are assigned to moving objects and they move according to the optical flow. Dynamic agents are responsible for calculating the information about social interaction between neighbors, using a Social interaction Force Magnitude (SFM) model and the potential energy of interaction. The proposed system divides each video into non-overlapping blocks and places a static agent and a dynamic agent in the center of each block of the initial frame. Then, the optical flow field between pairs of consecutive frames is computed, extracting information from the behavior of individuals and groups, using the static and dynamic agents. Feature descriptors are created from the extracted information of each block and then mapped to codewords by a clustering process. Codewords from all the blocks plus the bag-of-word method are used to create feature word vectors. The feature word vectors are then processed by a trained SVM classifier and determine the abnormality of the scene. The system was implemented in Visual C++, using OpenCV and LIBSVM libraries. The reported results allowed concluding that the proposed system outperformed the SFM method over PETS 2009 and UCSD datasets. In addition, the authors argued that static agents help to detect quick movements, movements in restricted areas and abnormal behaviors of individual subjects, while dynamic agents help to detect disordered movements (such as panic situations) and separation movements, which are poorly detected by simple agent systems. [PERSON] et al. [27] presented a method for detecting abnormal events in crowded scenes based on modeling motion and context information. Low- or medium-level visual information from surrounding local regions was used as context information. The proposed method focuses on modeling the activity of a crowd using context and motion information, considering that all objects or regions with detected motion provide context information. The method extracts dense trajectories and applies noise filters to filter non-typical trajectories. After that, Multiscale Block-Local Binary Pattern coding on Three Orthogonal Plans (MB-LBP-TOP) was used to encode local context information, and the Multiscale Histogram of Frequency Coefficient (MHFC) feature descriptor was used to describe the motion information and extract dense trajectories. Authors claimed that extracting dense trajectories, preferably with noise screening, using MHFC descriptors has two benefits: (i) the method achieves independence of the processed data and, (ii) it is able to better capture the motion characteristics of trajectories. Based on context patterns and motion information, the model is trained using sparse coding and sparse reconstruction cost is applied to classify events into normal and abnormal. The system was implemented in MATLAB and the experimental analysis was performed over UCSD and Subway datasets, obtaining a processing time of 3.7 and 4.2 s/frame, respectively. The authorsconcluded that the proposed system computed better results than previously developed methods, arguing that improvements are due to the fact that the proposed model does not take into account only the movement of the crowd, but it also uses context information contributed by surrounding objects.