# Temporal Saliency-Based Suspicious Behavior Pattern Detection
## 1 Introduction
Ordinary behavior refers to actions that do not attract people's attention when people perceive some sort of movement [8]. Therefore, surveillance systems detect suspicious behavior using characteristic patterns for various behaviors, which are generally opposed to ordinary behaviors. There have been many studies on abnormal behavior detection using different approaches such as spatio-temporal features [8; 9; 10; 11; 12; 13; 14; 15; 16] and machine learning techniques [17; 18; 19; 20; 21; 22; 23; 24; 25]. As a high-dimensional feature is essential to better represent the suspicious behavior pattern, many methods based on spatio-temporal information such as optical flow [8], spatio-temporal gradient [9], the social force model [10], chaotic invariant [11], and sparse representation [12] have been studied. It does not require any training learning process, so it has less computation, which can be used in real-time detection [8]. The method described in [9] extracts moving objects from video sequence first and then tracks moving objects to detect their overlapping. Once an overlapping area is detected, the clutter model is built up based on the changes of spatio-temporal features to detect abnormal behavior. An abnormal pattern detecting method based on spatiotemporal volume has been presented in [13]. It calculated the likelihood by analyzing the area occupying a relatively large part of the periphery and transformed it into the form of a codebook, thereby reducing the time required for the calculation. This method is competitive with other methods because it does not require background/foreground segmentation and tracking calculations. However, it is difficult to use this method in an image in which various kinds of abnormal conditions may exist, because the threshold value necessary for detecting abnormal patterns has to be individually calculated and applied experimentally for each image. The method described in [14] detects abnormal crowd behavior based on a combined approach of energy model and threshold. It used the optical flow method to estimate displacement vectors of moving crowd and the computation of crowd motion energy. The crowd motion energy was further modified by crowd motion intensity. The method described in [15] also extracts the motion vector using the optical flow from the segmented image with foreground and background; then, the motion vector with a large change was detected and learned by principal component analysis (PCA). However, data loss can occur due to noise in the process of separating the foreground and background from actual images. Abnormal behavior detection using an interest point by simply monitoring the change of topological structure has been presented in [16]. Two new methods for the analysis of boundary point structure and the extraction of a critical point from the partial motion fields were introduced and both methods were used to build the global topological structure of the crowd motion. Machine learning techniques for detecting unusual events have been presented in [17; 18; 19; 20; 21; 22; 23; 24; 25; 26]. These methods also employ the feature extraction process but use trained data that came out of the learning process. The method described in [17] detects multiple anomalous activities with key features such as speed, direction, centroid, and dimensions, and these help to track an object in video frames. It also employed problem domain knowledge rules in order to distinguish activities and the dominant behavior of activities. In [18], a video frame is divided into several segments of equal size, and the features that were extracted from each segment were clustered using unsupervised learning. Then, the clusters smaller than this were classified as abnormal behavior. In this method, unusual phenomena that do not follow the general statistics are judged as abnormal behavior. However, when there is only abnormal behavior, not ordinary behavior, it is highly unlikely that abnormal behavior can be detected.