# Vision Transformer-Based Tailing Detection in Videos
## 1 Introduction  
Tailing is a situation in which one pedestrian follows another pedestrian in the same direction for some amount of time. The intention of a tailing person can range from assaulting, snatching, or even kidnapping. According to the statistics released by United Nations Office on Drugs and Crime (UNODC) [1], between 2006-2008, there were 4671 kidnapping events in Canada, 10,509 kidnapping events in Turkey, 2034 kidnapping events in the United Kingdom, and 23,991 kidnapping events in India. Figure 1 shows two of the alleged kidnapping attempts (on left), and three snatching cases (on right). The events given in Figure 1 are recorded using surveillance cameras, which are, presently, cheaper to deploy, and comes with an easier installation process. According to a survey [2], the surveillance cameras installed in 2016 worldwide will produce approximately 566 GB of data in one day. This rapid growth of surveillance video data presents higher challenges for video processing and understanding. In addition, the manual methods of monitoring also put limits to the effectiveness of these surveillance systems. Searching for abnormal incident in recorded sequences using human supervisors is tedious, and time-consuming work. Thus, the development of computer vision techniques of event-detection [3], video retrieval [4] and video summarizing [5] are eminent part of modern surveillance systems. Since tailing detection can prevent crimes such as assault and kidnapping in advance, tailing detection becomes an important problem to solve. However, tailing detection from surveillance videos is not an easy task, due to the diversity in the behavior and motion patterns of the suspect and victim. Moreover, it is difficult to distinguish suspected tailing people from normal people. Previous work on suspicious event detection in surveillance videos is mostly inspired by anomaly detection [6], where the anomalous events are defined as of low probabilities relative to normal events, or events that are inconsistent with normal samples, and those which deviate from the normal behavioral and appearance patterns. Basic anomaly detection methods consist of two major steps, (i) representation of the event using visual or behavioral features and (ii) establishment of the anomaly detection model that distinguish between normal and abnormal event. Examples of low-level visual or behavioral features include spatiotemporal 3D gradient features [7], multi-scale histogram of optical flow (MHOF) [8], structural context descriptor (SCD) [9], and mixture of dynamic textures (MDT) [10], while the examples of high-level features include object trajectory features [11] and object appearance characteristics [12]. Building an anomaly detection model refers to defining a set of rules or models to distinguish between normal and abnormal events, based on the input features. Cluster-based anomaly detection models [13] tend to cluster similar normal events together, and the events whose center lies farthest from the center of the cluster are regarded as abnormal events. Another method used as an anomaly detection model is the state inference model [14], where the assumption for a normal event is that the normal events undergo a fixed change over time, while abnormal events do not. Finally, the sparse reconstruction [8,15]-based anomaly detection models calculate the reconstruction error of events and expect a small error relative to the reconstruction error of the abnormal events. The problem of tailing detection differs from event classification methods [16], where the task is to classify the event into one of multiple classes such as \"attempting a board trick\", \"feeding an animal\" and \"making a sandwich\" based on detection of one or more primitive actions, such as \"jumping\", \"sliding\" and \"flipping\". Another related problem is the human-interaction recognition [17], where the goal is to recognize the activity of  
Figure 1: Real world example of tailing, in the streets of London (source-YouTube).