Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training

To mitigate the adverse effects of hard samples on the training of the Flying Bird Object Detection (FBOD) model for surveillance videos, we propose a Co-Paced Learning Based on Confidence (CPL-BC) strategy and apply this strategy to the training process of the FBOD model. This strategy involves maintaining two models with identical structures but different initial parameter configurations, which collaborate with each other to select easy samples with prediction confidence exceeding a set threshold for training. As training progresses, the strategy gradually lowers the threshold, allowing more samples to participate, enhancing the model's ability to recognize objects from easy to hard. Before applying the CPL-BC strategy to train the FBOD models, we initially trained the two FBOD models to equip them with the capability to assess the difficulty level of flying bird object samples. Experimental results on two different datasets of flying bird objects in surveillance videos demonstrate that, compared to other model learning strategies, CPL-BC significantly improves detection accuracy, verifying the effectiveness and advancement of this method.

FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video

A Flying Bird Dataset for Surveillance Videos (FBD-SV-2024) is introduced and tailored for the development and performance evaluation of flying bird detection algorithms in surveillance videos. This dataset comprises 483 video clips, amounting to 28,694 frames in total. Among them, 23,833 frames contain 28,366 instances of flying birds. The proposed dataset of flying birds in surveillance videos is collected from realistic surveillance scenarios, where the birds exhibit characteristics such as inconspicuous features in single frames (in some instances), generally small sizes, and shape variability during flight. These attributes pose challenges that need to be addressed when developing flying bird detection methods for surveillance videos. Finally, advanced (video) object detection algorithms were selected for experimentation on the proposed dataset, and the results demonstrated that this dataset remains challenging for the algorithms above. The FBD-SV-2024 is now publicly available: Please visit https://github.com/Ziwei89/FBD-SV-2024_github for the dataset download link and related processing scripts.