Zexi Hua

Zi-Wei Sun, Ze-Xi Hua, Heng-Chao Li et al.

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.

Zi-Wei Sun, Ze-Xi Hua, Heng-Chao Li et al.

The flying bird objects captured by surveillance cameras exhibit varying levels of recognition difficulty due to factors such as their varying sizes or degrees of similarity to the background. To alleviate the negative impact of hard samples on training the Flying Bird Object Detection (FBOD) model for surveillance videos, we propose the Co-Paced Learning strategy Based on Confidence (CPL-BC) and apply it to the training process of the FBOD model. This strategy involves maintaining two models with identical structures but different initial parameter configurations that collaborate with each other to select easy samples for training, where the prediction confidence exceeds a set threshold. As training progresses, the strategy gradually lowers the threshold, thereby gradually enhancing the model's ability to recognize objects, from easier to more hard ones. Prior to applying CPL-BC, we pre-trained the two FBOD models to equip them with the capability to assess the difficulty 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, thereby verifying the method's effectiveness and advancement.