A serial dual-channel library occupancy detection system based on Faster RCNN

The phenomenon of seat occupancy in university libraries is a prevalent issue. However, existing solutions, such as software-based seat reservations and sensors-based occupancy detection, have proven to be inadequate in effectively addressing this problem. In this study, we propose a novel approach: a serial dual-channel object detection model based on Faster RCNN. Furthermore, we develop a user-friendly Web interface and mobile APP to create a computer vision-based platform for library seat occupancy detection. To construct our dataset, we combine real-world data collec-tion with UE5 virtual reality. The results of our tests also demonstrate that the utilization of per-sonalized virtual dataset significantly enhances the performance of the convolutional neural net-work (CNN) in dedicated scenarios. The serial dual-channel detection model comprises three es-sential steps. Firstly, we employ Faster RCNN algorithm to determine whether a seat is occupied by an individual. Subsequently, we utilize an object classification algorithm based on transfer learning, to classify and identify images of unoccupied seats. This eliminates the need for manual judgment regarding whether a person is suspected of occupying a seat. Lastly, the Web interface and APP provide seat information to librarians and students respectively, enabling comprehensive services. By leveraging deep learning methodologies, this research effectively addresses the issue of seat occupancy in library systems. It significantly enhances the accuracy of seat occupancy recognition, reduces the computational resources required for training CNNs, and greatly improves the effi-ciency of library seat management.

Faster and More Accurate Trace-based Policy Evaluation via Overall Target Error Meta-Optimization

To improve the speed and accuracy of the trace based policy evaluation method TD({\lambda}), under appropriate assumptions, we derive and propose an off-policy compatible method of meta-learning state-based {\lambda}'s online with efficient incremental updates. Furthermore, we prove the derived bias-variance tradeoff minimization method, with slight adjustments, is equivalent to minimizing the overall target error in terms of state based {\lambda}'s. In experiments, the method shows significantly better performance when compared to the existing method and the baselines.