Pedestrian Detection Based on Multi-Block Local Binary Pattern and Biologically Inspired Feature


  •  Aminou Halidou    
  •  Xinge You    
  •  Bachirou Bogno    

Abstract

Nowadays pedestrian detection plays an important role in security and driving assistance. Detecting moving object is complex, and some of the detection methods are comparatively ineffective and slow.  In relation to human detection it is very useful to combine independent information sources, such as appearance and motion. To achieve acceptable detection performance, we propose inter-frames differencing image to compute the region of interest, and MB-BIF to extract features. The MB-BIF approach combines two well-known methods, the Multi-Block Local Binary Pattern and Biologically Inspired Method.  We evaluate the performance of different features descriptors on different databases, and our method shows good efficiency.



This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1913-8989
  • Issn(Onlne): 1913-8997
  • Started: 2008
  • Frequency: quarterly

Journal Metrics

(The data was calculated based on Google Scholar Citations)

Google-based Impact Factor (2018): 18.20

h-index (January 2018): 23

i10-index (January 2018): 90

h5-index (January 2018): 11

h5-median(January 2018):17

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