Target Field of View Prediction Using Artificial Pheromones for People Reidentification

Abstract

People reidentification is a fundamental task in automated video surveillance based on computer vision. Reidentification happens when a person seen in a field of view is the same that has been observed in other fields of view. A person who has disappeared from one field of view can appear in any other within a camera network. Instead of looking for the person in all neighboring fields of view, for an intelligent video surveillance system, it is more practical to predict which of the neighboring camera views the person could appear. This prediction can become achieved by learning the paths the person usually follows in the camera network. The ant colony optimization technique has properties that can get exploited for this purpose; precisely, the accumulation and evaporation of artificial pheromones are used to learn the paths. After the learning process, the proposed method can make predictions every time that the person leaves a field of view. Such prediction is evaluated to obtain feedback and further tune the learning process. The path followed by the person becomes obtained by tracking their face image within and between fields of view using correlation filters as descriptors. The results obtained from an extensive experiment show that the field of view that the person selects to visit can be successfully predicted using artificial pheromones, and thus, reduce the resources that require reidentification.