An Entropy Model for Loiterer Retrieval across Multiple Surveillance Cameras

Abstract

Loitering is a suspicious behavior that often leads to criminal actions, such as pickpocketing and illegal entry. Tracking methods can determine suspicious behavior based on trajectory, but require continuous appearance and are difficult to scale up to multi-camera systems. Using the duration of appearance of features works on multiple cameras, but does not consider major aspects of loitering behavior, such as repeated appearance and trajectory of candidates. We introduce an entropy model that maps the location of a person's features on a heatmap. It can be used as an abstraction of trajectory tracking across multiple surveillance cameras. We evaluate our method over several datasets and compare it to other loitering detection methods. The results show that our approach has similar results to state of the art, but can provide additional interesting candidates.

The future of biometrics technology: from face recognition to related applications

“…Second, although this automated system could discover frequent loiterer candidates, it was still far from making a clear decision to detect real loiterers. Therefore, we extended the AntiLoiter system to analyze the characteristics of the appearance patterns of potential loiterer candidates [67]. We developed a novel analytical model by utilizing the mathematical entropy to capture the change of movement, durations, and re-appearances of loiterer candidates, which enables us to understand the common characteristics of behavior patterns regarding true loiterers.…”

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“…Moreover, according to the infancy of individual action recognition methodologies, the program's stability is impossible to ensure. Motion trajectories [6], shape characteristics, texture features [7], optical flow characteristics, and other image information are employed in behavioural analysis approaches focused on low-level picture information. This method, characterized by simplicity and minimal complexity, specifies the target's behaviours from a macro perspective.…”

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“…And it is difficult to guarantee the stability of this kind of method due to the immaturity of the human posture estimation technology. On the other hand, in the behavior analysis methods based on low-level image information, motion trajectory [6], shape features [7], texture features [8], optical flow features, and other image information are used to perform the behavior analysis. This kind of method describes the target's behavior from a macroperspective, and it has the characteristics of simplicity and low complexity.…”