Type classification, color estimation, and specific target detection of moving targets on public streets


This paper describes a vision system that recognizes moving targets such as vehicles and pedestrians on public streets. This system can: (1) classify targets {vehicle, pedestrian, others} and, for “vehicles,” discriminate vehicle types and (2) estimate the main colors of targets. According to the input images to the system, the categories of targets are set as {mule (golf cart for workers), sedan, van, truck, pedestrian (single or plural), and other (such as noise)}. Their colors are set as six color groups {red, orange, yellow; green; blue, light blue; white, silver, gray; dark blue, dark gray, black; dark red, dark orange}. In this experiment, we collected images of targets from 9: 00 a.m. to 5: 00 p.m. on sunny and cloudy days as system training samples. The recognition ratio was 91.1% under the condition that both the recognition results of type and color agreed with the operator’s judgment. In addition, the system can detect predefined specific targets such as delivery vans, post office vans, and police cars by combining recognition results for type and color. The recognition ratio for specific targets was 92.9%. For the classification and estimation of targets, we employed a statistical linear discrimination method (linear discriminant analysis, LDA) and a nonlinear decision rule (weighted K-nearest neighbor rule, K-NN).

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  1. 1

    Koller D, Weber J, Huang T, Malik J, Ogasawara G, Rao B, Russel S (1994) Towards robust automatic traffic scene analysis in real-time. In: Proc. international conference on pattern recognition, pp 126-131

  2. 2

    Beymer D, McLauchlan P, Coifman B, Malik J (1997) A real-time computer vision system for measuring traffic parameters. In: Proc. international conference on computer vision and pattern recognition, pp 495-501

  3. 3

    Mantri S, Bullock D, Garret J, Jr (1997) Vehicle detection using a hardware-implemented neural net. IEEE Expert 12(1):15-21

    Google Scholar 

  4. 4

    Ikeda T, Ohnaka S, Mizoguchi M (1996) Traffic measurement with a roadside vision system - individual tracking of overlapped vehicles. In: Proc. international conference on pattern recognition, pp 859-864

  5. 5

    Xia Limin (2002) Vehicle shape recovery and recognition using generic models. In: Proc. 4th world congress on intelligent control and automation, pp 1055-1059

  6. 6

    Wei Wu, Zhang QiSen, Wang Mingjun (2001) A method of vehicle classification using models and neural networks. In: Proc. IEEE 53rd conference on vehicular technology (VTC2001), 4:3022-3026

  7. 7

    Gupte S, Masoud O, Martin RFK, Papanikolopoulos NP (2002) Detection and classification of vehicles. IEEE Trans Intell Transport Syst 3:37-47

    Google Scholar 

  8. 8

    Lipton A, Fujiyoshi H, Patil R (1998) Moving target classification and tracking from real-time video. In: Proc. workshop on application of computer vision, pp 8-14

  9. 9

    Ohta Y, Kanade T, Sakai T (1980) Color information for region segmentation. Comput Graph Image Process 13:222-241

    Google Scholar 

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Correspondence to Osamu Hasegawa.

Additional information

Received: 11 March 2004, Accepted: 11 August 2004, Published online: 20 December 2004

Correspondence to: Osamu Hasegawa

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Hasegawa, O., Kanade, T. Type classification, color estimation, and specific target detection of moving targets on public streets. Machine Vision and Applications 16, 116–121 (2005). https://doi.org/10.1007/s00138-004-0163-4

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  • Linear Discriminant Analysis
  • Recognition Result
  • Vehicle Type
  • Color Group
  • Public Street