# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 3 Method
### Parallel OWOD Framework with Uncertainty Mitigation
#### 3.2.1 Objectness-Driven Discovery Module  
To address the challenge posed by the unknown discovery uncertainty introduced from the strategy of unknown pseudo-labeling, we introduced an objectness-driven discovery method to learn the unknown objects certainly from the deterministic supervision of known classes.  
Since both unknown and known classes inherently share the common characteristic of being \"generalized objects\", we can learn objectness from known classes to guide the learning of unknown classes. We simply convert all known object classes to a generalized class and only distinguish them from background regions without objects, extracting generalized features from all known classes while attenuating discriminative features to enhance the model's potential for object discovery. Through this approach, the detector can not only detect known objects but also discover other unknown objects with universities that are similar to known ones. Compared to previous methods for unknown object discovery, our objectness-driven discovery module extracts stable objectness from the supervised information of known classes, rather than manually selecting potential regions for unknown objects from the background. Therefore, our framework effectively reduces the uncertainties and enhances the detection performance of unknown objects.  
Specifically, during the training phase of the detector, the instances of all known classes except the background region are converted to a universal class \"generalized object\". Given  
Figure 2: Architecture of our parallel OWOD framework. The parallel OWOD framework comprises an objectness-driven discovery module, a known object detector, and a collaborative open-world classifier.  
the \(i\)-th instance of known classes \(\mathbf{x}_{i}^{(K^{\prime})}\) with its annotations \(\mathbf{y}_{i}^{(K^{\prime})}\), the supervision label used for classification regularization could be denoted as \(\widetilde{\mathbf{y}}_{i}\):  
\[\widetilde{\mathbf{y}}_{i}=\mathbf{y}_{i}^{(G^{\prime})}=[l^{(G^{\prime})},b_{ i}^{(K^{\prime})}] \tag{1}\]  
where \(\mathbf{y}_{i}^{(G^{\prime})}\) denotes the generalized annotation of the instance after convert, and \(l^{(G^{\prime})}=o\) denotes the universal label \"generalized object\" of every known instance. The classification regularization for the objectness-driven discovery module can be presented as follows:  
\[\widetilde{\ell}_{\text{universal}}^{cls} =-\sum_{i}\widetilde{\mathbf{y}}_{i}\log\mathbf{x}_{i}^{(K^{ \prime})} \tag{2}\] \[=-\sum_{i}\mathbf{y}_{i}^{(G^{\prime})}\log\mathbf{x}_{i}^{(K^{ \prime})}\]  
where \(\widetilde{\ell}_{\text{universal}}^{cls}\) is the loss of classification regularization calculated by the universal label for the objectness-driven discovery module.  
Through the objectness-driven discovery module, our framework is capable of identifying generalized objects possessing the attribute of \"being an object\" in the input image, including numerous unknown objects that have never been encountered in the training phase.