# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
###### Abstract
Previous OWOD works mainly realized unknown class detection by designing complex unknown proposal generation mechanisms. The ORE method [14] incorporates an unknown object-aware Region Proposal Network (RPN), selecting top-k region proposals sorted by the objectness of the RPN from background as unknown objects, thereby endowing the model with the ability to detect unknown objects. [PERSON] et al. [15] further introduces an auxiliary proposal advisor that combines an unknown-aware RPN with the selective search method to generate more accurate pseudo-labels for unknown categories. Building on the recent success of closed-set detector D-DETR [16], OW-DETR [17] proposes an unknown pseudo-labeling method that selects query boxes with high attention scores which not match with the ground truth of known class. However, in these OWOD works, two major uncertainties that limit the detection performance are commonly observed: the unknown discovery uncertainty and the known discrimination uncertainty. The unknown discovery uncertainty indicates the uncertain bias from the manual generation of pseudo-labels for unknown objects. In the previous OWOD works, the unknown regions were largely derived from high-score region proposals generated by RPN, which may potentially cover some background areas that introduce great uncertain bias. The known discrimination uncertainty arises from the uncertain perturbations that the learning of unknown classes introduces to the features of known class discrimination. The differentiation among known classes primarily relies on discriminative features, whereas the learning of unknown classes guides the model to extract more generic features among objects, which are only distinguished from the background. Therefore, while granting the model the ability to detect unknown classes, previous methods often resulted in an uncertain perturbation in the discrimination performance of known classes. These two kinds of uncertainty have impeded further improvements in detection performance in open-world scenarios, making it challenging to apply them to real-world environments like campus. In this paper, we introduce a Parallel OWOD Framework to mitigate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. Specifically, the framework employs a parallel architecture to decouple the process of learning unknown classes from the detection of known classes, thereby preventing the influence of uncertain features learned through unknown guidance on the known classes. Through the parallel architecture, our framework successfully mitigates the known discrimination uncertainty during open-world training. To alleviate the unknown discovery uncertainty, we have designed and added an objectness-driven discovery module to replace the uncertain unknown object selection, driving the model to focus on the objectness of various objects derived from the deterministic supervision of known classes. Therefore, the Objectness-Driven Discovery Module can learn objectness scores from stable supervision of known classes. For all objects, including both known and unknown classes, their objectness scores are expected to be significantly higher than those of the background regions. Consequently, the objectness-driven discovery module is capable of identifying potential objects with objectness more certainly from the background, including unknown objects. Furthermore, the output from the objectness-driven discovery module and a close-world known object detector is subsequently reconciled via a collaborative open-world classifier, which eliminates redundant unknown detection instances and facilitates collaborative learning of known and unknown classes. As shown in Figure 1, this framework may provide a powerful tool for educators and administrators to effectively monitor campus and manage classroom behaviors.