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Page 18 Xu et al. J Surveill Secur Saf 2020;1:16-33 I http://dx.doi.org/10.20517/jsss.2020.04
[3]
Wu et al. proposed a population abscess detection method based on Bayesian model. The method is
mainly based on the optical flow extraction motion feature, directly simulates the crowd movement with
the conditional density function, and uses the Bayesian classification formula to determine whether there
is an abnormal crowd escape event. Their experiments show that this method can accurately detect the
abnormal behavior of the crowd, but it is not appropriate for the crowded scene. The global exception can
only determine whether there is an abnormality in the monitoring screen. In actual applications, it is often
necessary to locate the specific location where the abnormality occurs. Based on this, many scholars have
proposed a local anomaly detection method, which usually divides the video picture into many small areas
and locates the specific location of the abnormality through the abnormal situation of all small areas.

[4]
Biswas et al. proposed an abnormal event detection method based on the social force model. In the
social force model, everyone in the group is simultaneously influenced by the individual’s desired force
and social interaction. The direction in which the individual expects the force indicates the direction of the
movement desired by the individual, and the direction of the social interaction indicates the direction in
which the environment, the pedestrian, etc. influence the individual. The direction of the two forces is the
actual direction of the individual in the crowd. A particle flow calculation method based on optical flow
is proposed to calculate the interaction force and solve the problem that the force calculation in the crowd
is difficult due to serious crowding and occlusion. After modeling the crowd, the LDA model is used to
determine the normal and abnormal frames in the video.

[5]
Cong et al. proposed anomalous event detection for the sparse reconstruction cost (SRC) model. In
this method, three different types of multi-scale optical flow histograms are extracted for different local
anomalous behaviors and global anomalous behaviors. After extracting features from normal frame images,
the feature sets are composed of the extracted features, and the redundant information in the dictionary
set is eliminated by an optimized method to form an optimal dictionary set. At the same time, the method
uses the best dictionary set to judge whether each frame of the test set has abnormal behavior through
[6]
the SRC method. Li et al. proposed a mixed dynamic texture model (MDT) to detect anomalies in
dense populations. The MDT model performs a time-space block on a video sequence to detect whether
an exception has occurred. In the time anomaly detection, the local distribution of the image intensity
is simulated based on the foreground extraction of the Gaussian mixture model. In the spatial anomaly
detection, the local region where an abnormality may occur is discriminated based on the principle of
image saliency (the spatial position of the abnormality is higher than a certain threshold).

Song et al. proposed a chaotic invariant algorithm. This paper proposes a human flow model that is
[7]
applicable in both structured and unstructured scenarios. First, the particles are advected based on the
optical flow, and the trajectory of the human flow is represented by the trajectory of the aggregated
particles. Then, all representative trajectories are quantified using a blunt invariant, and a model is
trained using the quantized chaotic set. Finally, the maximum likelihood estimation is used to identify
abnormalities and normal behaviors in the population.

[8]
Kratz and Nishino proposed a framework for modeling local temporal and spatial motion behaviors in
dense population scenarios based on Hidden Markov Models (HMM). In the training phase, the temporal
relationship between local motion patterns is extracted by a distribution-based HMM, and the spatial
relationship is modeled by a coupled HMM. In the test phase, the anomaly event is the statistical deviation
in the same scene in the video sequence. Their experiments show that HMM is suitable for analyzing more
intensive scenes, and an HMM is established for each small area, which indicates that the method is only
appropriate for a limited variety of normal behavior.

Despite the increasing popularity of video surveillance equipment and the maturity of intelligent
surveillance technology, real-time problems are faced in practical applications. At present, although many