# Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information arXiv: https://arxiv.org/abs/2301.01917 ยท HTML: https://mlsanity.com/paper/2301.01917/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 48 references that are in the map. - First posted on arXiv 5 Jan 2023. ## Builds on (most cited first, 29 of 29) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014) - [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018) - [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015) - [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015) - [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018) - [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013) - [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020) - [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015) - [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016) - [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021) - [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016) - [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018) - [Deep Feature Flow for Video Recognition](https://mlsanity.com/paper/1611.07715/) (2016) - [Flow-Guided Feature Aggregation for Video Object Detection](https://mlsanity.com/paper/1703.10025/) (2017) - [Sequence Level Semantics Aggregation for Video Object Detection](https://mlsanity.com/paper/1907.06390/) (2019) - [TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2201.05047/) (2022) - [Seq-NMS for Video Object Detection](https://mlsanity.com/paper/1602.08465/) (2016) - [T-CNN: Tubelets with Convolutional Neural Networks for Object Detection from Videos](https://mlsanity.com/paper/1604.02532/) (2016) - [Towards High Performance Video Object Detection](https://mlsanity.com/paper/1711.11577/) (2017) - [Temporal RoI Align for Video Object Recognition](https://mlsanity.com/paper/2109.03495/) (2021) - [Object Detection from Video Tubelets with Convolutional Neural Networks](https://mlsanity.com/paper/1604.04053/) (2016) - [End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2105.10920/) (2021) - [Mobile Video Object Detection with Temporally-Aware Feature Maps](https://mlsanity.com/paper/1711.06368/) (2017) - [Impression Network for Video Object Detection](https://mlsanity.com/paper/1712.05896/) (2017) - [Object Detection in Videos by High Quality Object Linking](https://mlsanity.com/paper/1801.09823/) (2018) - [Object Detection in Video with Spatial-temporal Context Aggregation](https://mlsanity.com/paper/1907.04988/) (2019) ## Built on by (most cited first, 5 of 5) - [FBD-SV-2024: Flying Bird Object Detection Dataset in Surveillance Video](https://mlsanity.com/paper/2409.00317/) (2024) - [A Flying Bird Object Detection Method for Surveillance Video](https://mlsanity.com/paper/2401.03749/) (2024) - [Real-Time Detection for Small UAVs: Combining YOLO and Multi-frame Motion Analysis](https://mlsanity.com/paper/2411.02582/) (2024) - [Self-Paced Learning Strategy with Easy Sample Prior Based on Confidence for the Flying Bird Object Detection Model Training](https://mlsanity.com/paper/2412.06306/) (2024) - [Co-Paced Learning Strategy Based on Confidence for Flying Bird Object Detection Model Training](https://mlsanity.com/paper/2501.12071/) (2025) ## Abstract (arXiv) 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.