# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
###### Abstract  
The recent advancements in artificial intelligence have brought about significant changes in education. In the context of intelligent campus development, target detection technology plays a pivotal role in applications such as campus environment monitoring and the facilitation of classroom behavior surveillance. However, traditional object detection methods face challenges in open and dynamic campus scenarios where unexpected objects and behaviors arise. Open-World Object Detection (OWOD) addresses this issue by enabling detectors to gradually learn and recognize unknown objects. Nevertheless, existing OWOD methods introduce two major uncertainties that limit the detection performance: the unknown discovery uncertainty from the manual generation of pseudo-labels for unknown objects and the known discrimination uncertainty from perturbations that unknown training introduces to the known class features. In this paper, we introduce a Parallel OWOD Framework with Uncertainty Mitigation to alleviate the unknown discovery uncertainty and the known discrimination uncertainty within the OWOD task. To address the unknown discovery uncertainty, we propose an objectness-driven discovery module to focus on capturing the generalized objectness shared among various known classes, driving the framework to discover more potential objects that are distinct from the background, including unknown objects. To mitigate the discrimination uncertainty, we decouple the learning processes for known and unknown classes through a parallel structure to reduce the mutual influence at the feature level and design a collaborative open-world classifier to achieve high-performance collaborative detection of both known and unknown classes. Our framework provides educators with a powerful tool for effective campus monitoring and classroom management. Experimental results on standard benchmarks demonstrate the framework's superior performance compared to state-of-the-art methods, showcasing its transformative potential in intelligent educational environments. intelligent campus monitoring; uncertainty mitigation; objectness-driven object discovery; parallel network; open-world object detection; artificial intelligence +
Footnote †: journal: _sciences_  
0000-0002-0002-3276-1000-0002-3276-1000-0002-3276-1000-0002-3276-1000-0002-3276-1000-0002-3276 campus monitoring and class behavior detection. [PERSON] et al. [8] design an object detection algorithm to detect the raising hand action in real classroom scenarios. [PERSON] et al. [9] propose CNPH-Net with a multiscale feature extraction module to enhance the capability of small object detection in classroom scenarios. [PERSON] et al. [13] applied object detection and gaze-tracking technology to analyze the students' attention in the classroom. These methods are all based on traditional closed-set object detection techniques, meaning that the models can only detect objects that are already present in the training set. However, due to the openness and diversity of objects in real campus and classroom scenarios, instances of objects and behaviors not included in the training datasets may arise. Traditional object detection methods are ill-equipped to detect such unexpected objects, making it challenging to meet the demands of real-world campus monitoring. To address the challenges of detection in open environments, such as campuses and classrooms, a new object detection task, Open-World Object Detection (OWOD) [14], is proposed to focus on the detection of objects in real-world open environments. This problem requires detection models not only to detect known objects but also to discover and incrementally learn about previously unseen unknown objects during the training phase. In the classroom and similar education scenarios, the OWOD models are required to discover and locate unknown targets and behaviors, and subsequently engage in incremental learning, which helps to facilitate intelligent monitoring of the teaching process.