  A Flying Bird Object Detection algorithm Based on Motion Information
(FBOD-BMI) is proposed to solve the problem that the features of the object are
not obvious in a single frame, and the size of the object is small (low
Signal-to-Noise Ratio (SNR)) in surveillance video. Firstly, a ConvLSTM-PAN
model structure is designed to capture suspicious flying bird objects, in which
the Convolutional Long and Short Time Memory (ConvLSTM) network aggregated the
Spatio-temporal features of the flying bird object on adjacent multi-frame
before the input of the model and the Path Aggregation Network (PAN) located
the suspicious flying bird objects. Then, an object tracking algorithm is used
to track suspicious flying bird objects and calculate their Motion Range (MR).
At the same time, the size of the MR of the suspicious flying bird object is
adjusted adaptively according to its speed of movement (specifically, if the
bird moves slowly, its MR will be expanded according to the speed of the bird
to ensure the environmental information needed to detect the flying bird
object). Adaptive Spatio-temporal Cubes (ASt-Cubes) of the flying bird objects
are generated to ensure that the SNR of the flying bird objects is improved,
and the necessary environmental information is retained adaptively. Finally, a
LightWeight U-Shape Net (LW-USN) based on ASt-Cubes is designed to detect
flying bird objects, which rejects the false detections of the suspicious
flying bird objects and returns the position of the real flying bird objects.
The monitoring video including the flying birds is collected in the unattended
traction substation as the experimental dataset to verify the performance of
the algorithm. The experimental results show that the flying bird object
detection method based on motion information proposed in this paper can
effectively detect the flying bird object in surveillance video.
