Image detection algorithm for incident of discarded things in highway(PDF)
长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]
- Issue:
- 2017年05期
- Page:
- 81-88
- Research Field:
- 交通工程
- Publishing date:
Info
- Title:
- Image detection algorithm for incident of discarded things in highway
- Author(s):
- WANG Gui-ping; MA Li-wang; GUO Lu; WANG Hui-feng; ZHANG Tao
- 1. School of Electronic & Control Engineering, Chang’an University, Xi’an 710064, Shaanxi, China; 2. Xi’an ASN Technology Group Company, Xi’an 710068, Shaanxi, China
- Keywords:
- traffic engineering; image; algorithm; discarded thing; shadow detection
- PACS:
- U491
- DOI:
- -
- Abstract:
- In view of that the existing detection methods of highway abnormal events needed a large amount of calculation, and the processing speed was limited, this paper proposed a new method for incident detection of discarded things in highway and the shadow suppression algorithm for illegal vehicles. The algorithm used the preconditions of small difference in moving target position among 5-frame images of video sequence, and 2 times 3-frame difference were conducted at intervals to form 5-frame difference. On the basis of this, foreground target image could be got through twice “and” operation with the difference and gray scale detection threshold. The required parameters were obtained by calculation and identification of the threshold which was set by using gray value of the moving target changing area. Background-difference method of fusion threshold self-adaptation was used to obtain foreground target image. The two resulting images was processed using morphologic operator by “or” operation, and the fused foreground target image was obtained by denoising smooth so as to extract the moving target with high precision. Taking advantage of the visual invariance, the pixel values of the image were decomposed into luminance component and chrominance component for shadow detection, and detection and suppression of the shadow of vehicles under strong light was realized. The results show that the new algorithm can effectively deal with typical highway abnormal events, and the accident vehicle can be effectively segmented from the shadow. Moreover, the processing speed can reach 25 frames per second to meet real-time requirements.
Last Update: 2017-10-16