## 2 Related Work
### Crowd Behavior Detection Systems  
Many contributions have been proposed to describe crowd behavior using various models [63; 64; 65; 66; 67; 68; 69]. Crowd behavior detection is a challenging problem due to the arbitrary movements of individuals and groups, partial or full occlusions, different outlooks and behaviors, posture changes, and composite backgrounds [70; 71; 72; 73; 74; 75; 76]. To detect human behaviors automatically in crowded areas, [PERSON] et al. in [77] constructed a density function of optical flow based on class-conditional probability and described the motion of crowds using divergent centers and potential destinations so that anomalies can be detected on the basis of a Bayesian framework. However, the system is not effective for arbitrary movements or overlaps. [PERSON] et al. in [78] proposed a SIFT feature extraction technique, along with a Genetic Algorithm for optimal feature extraction; anomalies were detected by checking feature set movement behaviors. Their proposed system has a very high computational processing demand. [PERSON] et al. in [79] used a one-class SVM, along with features based on optical flow to detect crowd behavior; their system is limited by the accuracy limitations of optical flow estimation. [PERSON] et al. in [80] introduced a moved-pixels density-based statistical modeling approach for detecting abnormal crowd behavior. This system has low computational cost, but the efficiency decreases with increasing complexity of the situation being monitored, e.g., serious occlusions. [PERSON] et al. in [81] first detected humans and then the gaussian smoothing technique was used to detect anomalous behavior; however, the accuracy of the system decreases with illumination changes and occlusions because thresholding is used for detection. [PERSON] et al. in [82] introduced a behavior entropy model for detecting abnormal crowd behavior using spatio-temporal information, along with behavior certainty of pixels, but the system is vulnerable to certain misclassifications due to interclass similarities. [PERSON] et al. in [83] addressed the crowd behavior detection problem by using the pyramid LucasKanade optical flow [84] method based on location estimation of adjacent flow; however, the proposed method is not effective for an unstructured crowd. [PERSON] et al. in [85] placed a grid of particles on the image and introduced a social force model for detecting crowd behavior. [PERSON], [PERSON] et al. in [86] pursued two specific objectives: the derivation of a general mathematical structure based on appropriate developments of the kinetic theory suitable for capturing the main features of crowd dynamics and the derivation of macroscopic equations from the underlying mesoscopic description. [PERSON], [PERSON] et al. in [87] dealt with macroscopic modelling of crowd movements, particularly how non-local interactions are influenced by walls, obstacles, and exits. An ad hoc numerical algorithm, along with heuristic evaluation of its convergence, was also provided. [PERSON], [PERSON] et al. in [88] proposed scale estimation network SENet and head detection network. The SENet takes the input image and predicts the distribution of scales (in terms of histogram) of all heads in the input image, which are later on classified by a detection network.