A Detection Algorithm for Projectile Target

Abstract:

For consistency of performance in the shape of the projectile targets, a projectile target detection algorithm is presented based on HOG (Histogram of Oriented Gradient) characterization algorithm. First, detecting the bullet image corner, and secondly, by Mean-shift algorithm improves the corner position accuracy and reduces the number of corner points, finally, applying support vector machines to extract the projectile targets. Compared with the traditional small target detection algorithm, the algorithm describes the targets more accurately, along with better real-time performance. Simulation, the projectile target detection rate of over 80% and verify the effectiveness of the algorithm.

• Periodical: Advanced Materials Research (Volumes 1044-1045)
• Pages: 972-975
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1044-1045.972
• Online since: October 2014
• Authors: Zai Fei Shang, Chun Ping Wang
• Keywords: HOG Descriptor, Local Descriptor, Projectile Target Detection, Small Target Detection

References

[1] Wang Guoyou, Chen Zhenxue et al. A Review of Infrared Weak and Small Targets Detection under Complicated Background, Infrared Technology. Vol. 28, No. 5 (2006) pp.287-292.

[2] Peng Jiaxiong, Zhou Wenlin, Infrared Background Suppression for Segmenting and Detectiong Small Target, ACTA ElECTRONICA SINICA. Vol. 27, No. 12 (1999) pp.47-51.

[3] Liu Yunlong, Xue Lili et al. Infrared small targets detection using local mean, Infrared and Laser Engineering, Vol. 42, No. 3 (2013) pp.814-822.

[4] Zhang Hong, Zhao Baojun et al. The Real-time Detection of Infrared Weak Targets Under Comples Background, Systems Engineering and Electronics, Vol. 23, No. 8, (2001) pp.40-41.

[5] Wei Hongqiang, Feng Jinliang et al. Detection method for small moving target in sequence image, Chinese Joumal of Scientific Instrument, Vol. 29, No. 8 (2008) pp.1735-1738.

[6] Ye bin, Peng Jiaxiong, Small Target Detection Method Based on Morphology Top-Hat Operator, Journal of Image and Graphics, Vol. 7(A), No. 7 (2002) pp.638-642.

[7] Wu Wei, Peng Jiaxiong, The feature of small target and its invariance analysis in infrared image sequence, J. Huazhong Univ. of Sci. & Tech. (Nature Science Edition), Vol. 30, No. 3 (2002) pp.83-85.

[8] Gao Chenqiang, Tian Jinwen et al, Detection of infrared spot small targets against complex sky background by using GST, J. Huazhong Univ. of Sci. & Tech. (Nature Science Edition), Vol. 37, No. 7 (2009) pp.10-13.

[9] Lin Jianlin, Ping Xijian et al. Orientation-coherence-feature-based Method to Detect Small Target in Drift-scanning Image, ACTA AUTOMATICA SINICA. Vol. 39, No. 6 (2013) pp.875-882.

DOI: 10.3724/sp.j.1004.2013.00875

[10] Dalal N, Triggs B. Histograms of Oriented Gradients for Human Detection, Proc. IEEE Conference on Computer Vision and Pattern Recognition, Vol. 1, No. 2 (2005) pp.886-893.

DOI: 10.1109/cvpr.2005.177