Learning Object Detectors in Stationary Environments

Peter M. Roth, Sabine Sternig, Horst Bischof

Abstract: The most successful approach for object detection is still applying a sliding window technique, where a pre-trained classifier is evaluated on different locations and scales. In this chapter, we interrogate this strategy in the context of stationary environments. In particular, having a fixed camera position observing the same scene a lot of prior (spatio-temporal) information is available. Exploiting this specific scene information allows for (a) improving the detection performance and (b) for reducing the model complexity; both on reduced computational costs! These benefits are demonstrated for two different real-world tasks (i.e., person and car detection). In particular, we apply two different evaluation/update strategies (holistic, grid-based), where any suited online learner can be applied. In our case we demonstrate the proposed approaches for different applications and scenarios, clearly showing their benefits compared to generic methods.