This study evaluates seven prominent SIFT implementations for feature detection in Wide Area Motion Imagery (WAMI): Lowe's archived code, VLFeat, OpenCV, SIFT anatomy, CudaSIFT, SiftGPU, and PopSift. We use spatio-temporal patch animations, termed ThumbTracks, to assess each method's performance in terms of jitter, wandering, and track switches. Additionally, we analyze the clustering of SIFT descriptors using t-distributed stochastic neighbor embeddings. Our results reveal significant variations in the performance of different SIFT variants, with implications for their suitability in various WAMI applications. We provide recommendations for selecting the most appropriate SIFT implementation based on feature stability, computational efficiency, and accuracy requirements.
Fast, efficient and robust algorithms are needed for real-time visual tracking that could also run smoothly on the airborne embedded systems. Flux tensor can be used to provide motion-based cues in visual tracking. In order to use any object motion detection on a raw image sequence captured by a moving platform, the motion caused by the camera movement must be stabilized first. Using feature points to estimate the homography matrix between the frames is a simple registration method that can be used for the stabilization. In order to have a good homography estimation, most of the feature points should lay on the same plane in the images. However, when the scene has complex structures it becomes very challenging to estimate a good homography. In this work, we propose a robust video stabilization algorithm which allows the flux motion detection to efficiently identify moving objects. Our experiments show satisfactory results when other methods shown to fail on the same type of raw videos.
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