Real-time aerial targets detection algorithm based background subtraction

In his paper, we propose a new technique incorporates several innovative mechanisms for aerial target detection. The traditional algorithm has high time complexity, and when the target size changes, it has greater limitations. It is difficult to meet its critical real-time and accuracy requirements in practical application. Based on this, we propose the air target detection algorithm based background subtraction. Complete modeling of the video in the first frame, detect the target in second frame, the location and size of the target is obtained by connecting area detection. From the third frame, we use the size and position of target from last frame to open a window, and track the target in the window. Tracking target in the window, we can eliminate background interference and reduce the time consumption. Compared with the traditional algorithm, the proposed algorithm is experimentally proved real-time well, detecting and tracking efficient highly, and have a highly practical in practice.

[1]  Keiichi Tokuda,et al.  Realizing Tibetan speech synthesis by speaker adaptive training , 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.

[2]  Marc Van Droogenbroeck,et al.  ViBE: A powerful random technique to estimate the background in video sequences , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Yi-Ju Lin,et al.  Quantitative evaluation of violin solo performance , 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.

[4]  Marc Van Droogenbroeck,et al.  ViBe: A Universal Background Subtraction Algorithm for Video Sequences , 2011, IEEE Transactions on Image Processing.

[5]  Shi Zhong-ke A Method of Vehicle Tracking Based on GM(1,1) , 2006 .

[6]  Jen-Tzung Chien,et al.  Adaptive processing and learning for audio source separation , 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.

[7]  Lei Xie,et al.  Context-dependent deep neural networks for commercial Mandarin speech recognition applications , 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.

[8]  Waleed H. Abdulla,et al.  Joint discriminative learning of acoustic and language models on decoding graphs , 2013, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference.

[9]  Chen Song-qiao Improvement of Warshall Algorithm Based on Transitive Closure , 2005 .