Utilize Dense Optical Flow for Small Flying Targets Detection and Tracking
Keywords:
Small Target, Optical Flow, K-Means, Infrared Imaging, Real-time, TrackingAbstract
The detection of small targets remains a critical challenge within the field of image processing. Traditional techniques, such as image subtraction with frame-to-frame registration, suffer from high false alarm rates. Even state-of-the-art deep learning architectures, like YOLO and Masked R-CNN, exhibit limitations in this domain. In overextended distances, the inherent feature quality of small targets degrades significantly, leading to a scarcity of informative data for conventional detection algorithms. Consequently, accurate visual recognition becomes a particularly hard task.This work presents a novel detection approach that draws inspiration from the human visual attention mechanism. By leveraging dense optical flow, the model prioritizes moving objects within the scene, facilitating effective target detection. Furthermore, the proposed method employs K-Means clustering to achieve robust foreground-background separation based on color intensity characteristics. To address the limitations of dense optical flow with stationary targets, a dedicated tracking algorithm is also introduced. Our approach demonstrated a high level of accuracy (98%) when evaluated on unseen test data. Additionally, the algorithm functioned in real-time, enabling immediate processing.
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