KEYWORDS: Video, Detection and tracking algorithms, Target detection, Video surveillance, 3D vision, Defense and security, Feature extraction, Optoelectronics, 3D image processing, Optical tracking
For feature point detection with variable scale, rotation, variable illumination and variable 3D view port, a feature point
detection and tracking method combining scale invariant feature transform (SIFT) and KLT (Kanade-Lucas-Tomasi) is
proposed in this paper. SIFT feature point detection method is improved and it is used to detect feature points of image,
and then KLT method is used to track the feature points continuously. In order to verify the feasibility of the proposed
method, simulation experiments are carried out in real scene image sequences with different complexity using this
method, better results of detection and tracking are obtained and the obtained feature point is more stable than
conventional method.
This paper presents a novel method for automatically segmenting and detecting targets in complex environment using the
improved unit linking pulse coupled neural networks (ULPCNN) combining with contour tracking. On the one hand, the
typical ULPCNN model is improved including linear modulate, linear attenuation of dynamic threshold and the
attenuation parameter matrix Δ , which is more suitable for segmenting and detecting the target under complex
environment. On the other hand, we determine the iteration times and obtain the optimal segmentation result using
contour tracking based on maximum line contour point. In order to verify the efficiency, various simulations were
conducted for different images acquired from real scenes. Experimental results show, as compared to the conventional
approaches, the proposed method can overcome the drawbacks of PCNN and obtain the good results for segmenting and
detecting targets against complex background.
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