Brake light detection of front cars has become a very important issue in safety of transport systems in recent years. As an adjunct component of automatic braking or warning systems, recognition and discrimination of the brake lights using vehicle-mounted cameras provides early warning to avoid rear-end collisions for the vehicles. Therefore in this paper a single camera-based segmentation method is introduced for detecting the brake lights in nighttime cruising and discriminating them from the other lights, such as tail lights and turn lights. Basically, a novel system is put forward for discriminating brake lights which is initialised with capturing the frames of front car having the tail lights on, with a mounted camera. Subsequent to acquisition, image enhancement is applied to frames for whitening the red corona and darkening the rest including the centre of the light sources. Region of interests are determined using the cumulative contrast differences as well as rear light positions with calculation of white and black pixel ratios in coronas. Yet, the tail lights have the approximately same ratio for all distances, ratios of the brake lights are significantly high, resulting in discrimination of brake lights from others, for the vehicles cruising in the dark.

References

1. 1)
  - 28. Thammakaroon, P., Tangamchit, P.: ‘Improvement of forward collision warning in real driving environment using machine vision’, Int. J. Intell. Transp. Syst. Res., 2010, 8, (3), pp. 131–139.
2. 2)
  - 2. Cabrera, A., Gowal, S., Martinoli, A.: ‘A new collision warning system for lead vehicles in rear-end collisions’. IEEE Intelligent Vehicles Symp. (IV), 2012.
3. 3)
  - 8. Kavitha, K.V.N., Bagubali, A., Shalini, L.: ‘V2v wireless communication protocol for rear-end collision avoidance on highways with stringent propagation delay’. Proc. of Int. Conf. on Advances in Recent Technologies in Communication and Computing ARTCom ‘09, 2009.
4. 4)
  - 35. Teoh, S.S., Bräunl, T.: ‘Symmetry-based monocular vehicle detection system’, Mach. Vis. Appl., 2012, 23, (5), pp. 831–842 (doi: 10.1007/s00138-011-0355-7).
5. 5)
  - 11. Cheng, F., Danyang, Z., Zhijun, X.: ‘The study of vehicle's anti-collision early warning system based on fuzzy control’. IEEE Int. Conf. on Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010, vol. 3.
6. 6)
  - 26. Chen, D.Y., Chen, C.H.: ‘Visual-based spatiotemporal analysis for nighttime vehicle braking event detection’, Advances in Multimedia Modeling, Berlin Heidelberg, Springer, 2012, pp. 772–781.
7. 7)
  - 17. Schamm, T., von Carlowitz, C., Zollner, J.: ‘On-road vehicle detection during dusk and at night’. IEEE Intelligent Vehicles Symp. (IV), 2010.
8. 8)
  - 13. Kumar, K.S., Verghese, L., Mahapatra, K.K.: ‘Fuzzy logic based integrated control of anti-lock brake system and collision avoidance system using can for electric vehicles’. Proc. of IEEE Int. Conf. on Industrial Technology ICIT 2009, 2009.
9. 9)
  - 3. Sengupta, R., Rezaei, S., Shladover, S., Cody, D., Dickey, S., Krishnan, H.: ‘Cooperative collision warning systems: concept definition and experimental implementation’, J. Intell. Transp. Syst., 2007, 11, (3), pp. 143–155 (doi: 10.1080/15472450701410452).
10. 10)
  - 24. Chen, D.Y., Lin, Y.H., Peng, Y.J.: ‘Nighttime brake-light detection by Nakagami imaging’, IEEE Trans. Intell. Transp. Syst., 2012, 13, (4), pp. 1627–1637 (doi: 10.1109/TITS.2012.2199983).
11. 11)
  - 5. Kaempchen, N., Schiele, B., Dietmayer, K.: ‘Design of a dependable model vehicle for rear-end collision avoidance and its evaluation’. IEEE Instrumentation and Measurement Technology Conf. (I2MTC), 2010.
12. 12)
  - 14. Milanés, V., Pérez, J., Godoy, J., Onieva, E.: ‘A fuzzy aid rear-end collision warning/avoidance system’, Expert Syst. Appl., 2012, 39, (10), pp. 9097–9107 (doi: 10.1016/j.eswa.2012.02.054).
13. 13)
  - 20. Kim, S., Oh, S.Y., Kang, J., Ryu, Y., Kim, K., Park, S.C., Park, K.: ‘Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion’. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, 2005 (IROS 2005), 2005.
14. 14)
  - 19. Niknejad, H., Takahashi, K., Mita, S., McAllester, D.: ‘Vehicle detection and tracking at nighttime for urban autonomous driving’. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), 2011.
15. 15)
  - 1. Isaksson-Hellman, I., Lindman, M.: ‘The effect of a low-speed automatic brake system estimated from real life data’, Ann. Adv. Automot. Med., 2012, 56, pp. 231–2470.
16. 16)
  - 22. Chen, D.Y., Chen, C.H.: ‘Salient video cube guided nighttime vehicle braking event detection’, J. Vis. Commun. Image Represent., 2012, 23, (3), pp. 586–597 (doi: 10.1016/j.jvcir.2012.01.013).
17. 17)
  - 10. Kim, D., Park, K., Bien, Z.: ‘Hierarchical longitudinal controller for rear-end collision avoidance’, IEEE Trans. Ind. Electron., 2007, 54, (2), pp. 805–817 (doi: 10.1109/TIE.2007.891660).
18. 18)
  - 31. Lu, Y.Y., Han, C.C., Lu, M.C., Fan, K.C.: ‘A vision-based system for the prevention of car collisions at night’, Mach. Vis. Appl., 2011, 22, (1), pp. 117–127 (doi: 10.1007/s00138-009-0239-2).
19. 19)
  - 6. Kaempchen, N., Schiele, B., Dietmayer, K.: ‘Situation assessment of an autonomous emergency brake for arbitrary vehicle-to-vehicle collision scenarios’, IEEE Trans. Intell. Transp. Syst., 2009, 10, (4), pp. 678–687 (doi: 10.1109/TITS.2009.2026452).
20. 20)
  - 7. Naranjo, J., Gonzalez, C., Garcia, R., Pedro, T.d.: ‘Cooperative throttle and brake fuzzy control for acc + stop&go maneuvers’, IEEE Trans. Veh. Technol., 2007, 56, (4), pp. 1623–1630 (doi: 10.1109/TVT.2007.897632).
21. 21)
  - 23. Chen, D.Y., Lin, Y.H.: ‘Frequency-tuned nighttime brake-light detection’. Sixth Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP), 2010.
22. 22)
  - 16. Cabani, I., Toulminet, G., Bensrhair, A.: ‘Color-based detection of vehicle lights’. IEEE Proc. of Intelligent Vehicles Symp., 2005.
23. 23)
  - 34. Ortega, A.V., da Silva, I.N.: ‘Neural network based approach for automotive brake light parameter estimation’, Neural Information Processing, Berlin, Heidelberg, Springer, 2012, pp. 611–618.
24. 24)
  - 4. Ararat, Ö., Kural, E., Güvenç, B.: ‘Development of a collision warning system for adaptive cruise control vehicles using a comparison analysis of recent algorithms’. Intelligent Vehicles Symp., 2006.
25. 25)
  - 29. Su, J., Wang, X., Chen, R., Xu, G., Wu, S.: ‘Design and research on condition monitoring system of brake light of cars’. IEEE World Automation Congress (WAC), 2012.
26. 26)
  - 32. Yelal, M., Sasi, S., Shaffer, G., Kumar, A.: ‘Color-based signal light tracking in real-time video’. IEEE Int. Conf. on Video and Signal Based Surveillance 2006, AVSS ’06., 2006.
27. 27)
  - 18. Zheng, Z.H., Wang, B.: ‘On-road vehicle detection based on color segmentation and tracking using Harris-SIFT’, Adv. Mater. Res., 2012, 433, pp. 5334–5338 (doi: 10.4028/www.scientific.net/AMR.433-440.5334).
28. 28)
  - 25. Chen, D.Y., Lin, Y.H., Kuo, H.P.: ‘Scattering-based brake-light modeling and detection’. 14th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), 2011.
29. 29)
  - 12. Cheng, S., Fang, C.Y., Chen, C.P., Chen, S.: ‘Critical motion detection of nearby moving vehicles in a vision-based driver-assistance system’, IEEE Trans. Intell. Transp. Syst., 2009, 10, (1), pp. 70–82 (doi: 10.1109/TITS.2008.2011694).
30. 30)
  - 27. Casares, M., Almagambetov, A., Velipasalar, S.: ‘A robust algorithm for the detection of vehicle turn signals and brake lights’. IEEE Ninth Int. Conf. on Advanced Video and Signal-Based Surveillance (AVSS), 2012.
31. 31)
  - 33. Gong, J., Jiang, Y., Xiong, G., Guan, C., Tao, G., Chen, H.: ‘The recognition and tracking of traffic lights based on color segmentation and camshift for intelligent vehicles’. IEEE Intelligent Vehicles Symp. (IV), 2010.
32. 32)
  - 21. Thammakaroon, P., Tangamchit, P.: ‘Predictive brake warning at night using taillight characteristic’. IEEE Int. Symp. on Industrial Electronics, ISIE 2009, 2009.
33. 33)
  - 15. O'Malley, R., Glavin, M., Jones, E.: ‘Vehicle detection at night based on tail-light detection’, First Int. Symp. on Vehicular Computing Systems, Trinity College, Dublin, 2008.
34. 34)
  - 9. Lee, K., Peng, H.: ‘Evaluation of automotive forward collision warning and collision avoidance algorithms’, Veh. Syst. Dyn., 2005, 43, (10), pp. 735–751 (doi: 10.1080/00423110412331282850).
35. 35)
  - 30. Stanton, N., Lew, R., Boyle, N., Hope, R.J., Dyre, B.P., Bustamante, E.A.: ‘An implementation of a graded deceleration display in brake light warning’. Proc. of the Human Factors and Ergonomics Society Annual Meeting, 2011.

Corona segmentation for nighttime brake light detection

References

Related content