Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Generative Method for Textured Motion: Analysis and Synthesis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
A Shape Ontology Framework for Bird Classification
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Shape-and-Behavior Encoded Tracking of Bee Dances
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Detection and Tracking of Maneuverable Birds in Videos
CIS '08 Proceedings of the 2008 International Conference on Computational Intelligence and Security - Volume 01
Bird Objects Detection and Tracking on the Wild Field Circumstance
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 06
Spatio-temporal shape contexts for human action retrieval
IMCE '09 Proceedings of the 1st international workshop on Interactive multimedia for consumer electronics
IEEE Transactions on Image Processing
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
ViBe: A Universal Background Subtraction Algorithm for Video Sequences
IEEE Transactions on Image Processing
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Flying bird detection (FBD) is critical in avoiding bird-aircraft collisions. Most existing approaches rely on motion detection to identify the flying bird, since it is a typical moving object. However, when there exist other moving objects, those methods often fail to distinguish flying birds from those objects due to the insufficiency of feature description. In this paper, we introduce a novel hierarchical feature model exploiting shape and shape dynamics to improve the ability of representing a flying bird, and then apply it to the FBD problem. As the shape of a flying bird is very distinctive in geometric structures and could provide discriminating spatial information, an improved shape context feature descriptor is proposed at the lower level to capture the spatial relations in bird shape. Then the shape descriptor is extended into the spatio-temporal domain and a shape dynamics description is built at the higher level, in which a 4-state Markovian model is adopted and is learned from training sequences. Moreover, to build a mapping from the lower level to the higher level of the hierarchy, a shape similarity index (SSI) based matching mechanism is designed. We apply these two-level features for detecting flying bird for improved safety of aircrafts flying at low-altitude. The experimental results show that the proposed method is effective and outperforms three other existing vision-based FBD approaches.