Development of Nighttime Vehicle Detection System using 5-stage Cascade Classifier

  • Authors

    • Ranganathan Venkatasubramanian
    • Elangovan M
    2018-12-19
    https://doi.org/10.14419/ijet.v7i4.41.24303
  • ADAS, Cascade Classifier, Computer vision, Image-processing, Overtaking Assistance system, Vehicle detection during Nighttime,
  • There is one death in every four minutes due to road accidents in India. This paper is a contribution towards the development of Advanced Driver Assistance Systems such as overtaking assistance system, Adaptive cruise control system, etc., which in turn could reduce the number of road accidents. This paper proposes a three-step method to detect the vehicles in front during nighttime in one-way road. In the first step, the classifier is trained with negative samples and Region of Interest (ROI) marked positive samples. In the second step, input images are acquired and enhanced. In the third step, the enhanced input images are fed into the trained 5-stage cascade classifier, where the vehicles in front are detected and visually presented. This method can detect the vehicles in front during nighttime in one-way road with 65.6% accuracy.

     

     


     
  • References

    1. [1] Hulin Kuang, Long Chen, Feng Gu & Jiajie Chen (2016), Combining Region-of-Interest Extraction and Image Enhancement for Nighttime Vehicle Detection, IEEE Conference on Intelligent Systems.

      [2] Jiann-Der Lee, Jun-Ting Wu, Chung-Hung Hsieh & Jong-Chih Chien (2014), Close Range Vehicle Detection and Tracking by Vehicle Lights, 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).

      [3] Li-Chih Chen, Jun-Wei Hsieh, Shyi-Chy Cheng & Zi-Ran Yang (2015), Robust Rear Light Status Recognition Using Symmetrical SURFs, IEEE 18th International Conference on Intelligent Transportation Systems.

      [4] Ravi Kumar Satzoda & Mohan Manubhai Trivedi (2016), Looking at Vehicles in the Night: Detection and Dynamics of Rear Lights, IEEE Transactions on Intelligent Transportation Systems.

      [5] Senthil Kumar T. & Sivanandam S. (2012), An improved approach for detecting car in video using neural network model, Journal of Computer Science, vol. 8, pp. 1759-1768.

      [6] Senthil Kumar T. & Sivanandam S. (2012), A modified approach for detecting car in video using feature extraction techniques, European Journal of Scientific Research, vol. 77, pp. 134-144.

      [7] Zhiyong Cui, Shao-Wen Yang & Hsin-Mu (2015), A Vision-Based Hierarchical Framework for Autonomous Front-Vehicle Taillights Detection and Signal Recognition, IEEE 18th International Conference on Intelligent Transportation Systems.

  • Downloads

  • How to Cite

    Venkatasubramanian, R., & M, E. (2018). Development of Nighttime Vehicle Detection System using 5-stage Cascade Classifier. International Journal of Engineering & Technology, 7(4.41), 71-74. https://doi.org/10.14419/ijet.v7i4.41.24303