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An intelligent framework to detect and generate alert while cattle lying on road in dangerous states using surveillance videos

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Abstract

This paper presents the framework for early detection of cattle lying dead, blocking the road, or in a condition to cause an accident on highways based on video scrutiny. The suggested framework works with feature extraction, feature expression, and assessment criteria. It also includes a method that can identify the status of cattle on road i.e., cattle lying dead after an accident, a method for assessing stray cattle’s blocking the road, and rules for assessing other dangerous states. The framework is trained over 6000 images covering almost all the highway collision possibilities and tested on real images depicting different scenarios on highways. The proposed framework can achieve the mAP of up to 95.10% with precision and recall values of 0.98 and 0.94 respectively, which outperforms all other approaches taken into consideration. The outcomes of the study will assist the concerned authorities to take preventive steps to avoid road accidents or traffic-related issues.

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Data availability

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Abhilash PC, Singh N (2009) Pesticide use and application: an Indian scenario. J Hazard Mater 165(1–3):1–12

    Article  Google Scholar 

  2. Ajmi C, Zapata J, Elferchichi S, Zaafouri A, Laabidi K (2020) Deep learning technology for weld defects classification based on transfer learning and activation features. Adv Mater Sci Eng 2020:1–16

    Article  Google Scholar 

  3. Alcon M, Tabani H, Kosmidis L, Mezzetti E, Abella J, Cazorla FJ (2020, April) Timing of autonomous driving software: problem analysis and prospects for future solutions. In 2020 IEEE real-time and embedded technology and applications symposium (RTAS). IEEE. pp. 267-280

  4. Baviskar A (2011) Cows, cars and cycle-rickshaws: bourgeois environmentalists and the battle for Delhi's streets. In: Baviskar A, Ray R (eds), New Delhi: Routledge, pp 391–418

  5. Benjdira B, Khursheed T, Koubaa A, Ammar A, Ouni K (2019, February) Car detection using unmanned aerial vehicles: comparison between faster r-cnn and yolov3. In 2019 1st international conference on unmanned vehicle systems-Oman (UVS). IEEE. pp. 1-6

  6. Burghardt T, Calic J (2006) Analysing animal behaviour in wildlife videos using face detection and tracking. IEE Proc-Vision Image Signal Process 153(3):305–312

    Article  Google Scholar 

  7. Chahal KS, Dey K (2018) A survey of modern object detection literature using deep learning. arXiv preprint arXiv:1808.07256

  8. Chen JW, Lin WJ, Cheng HJ, Hung CL, Lin CY, Chen SP (2021) A smartphone-based application for scale pest detection using multiple-object detection methods. Electronics 10(4):372

    Article  Google Scholar 

  9. Cortes V, Jose C (2021) Joint neural networks for one-shot object recognition and detection (Doctoral dissertation, Queen Mary University of London)

  10. Dogru N, Subasi A (2018, February) Traffic accident detection using random forest classifier. In 2018 15th learning and technology conference (L&T). IEEE. pp. 40-45

  11. Elmaghraby AS, Losavio MM (2014) Cyber security challenges in smart cities: safety, security and privacy. J Adv Res 5(4):491–497

    Article  Google Scholar 

  12. Fabre-Thorpe M, Delorme A, Marlot C, Thorpe S (2001) A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. J Cogn Neurosci 13(2):171–180

    Article  Google Scholar 

  13. Gadd ME (2012) Barriers, the beef industry and unnatural selection: a review of the impact of veterinary fencing on mammals in southern Africa. Fencing for conservation, 153–186

  14. Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430

  15. Gibson DP, Campbell NW, Thomas BT (2003, September) Quadruped gait analysis using sparse motion information. In Proceedings 2003 international conference on image processing (cat. No. 03CH37429). IEEE. (Vol. 3, pp. III-333)

  16. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 580-587

  17. Goniewicz K, Goniewicz M, Pawłowski W, Fiedor P (2016) Road accident rates: strategies and programmes for improving road traffic safety. Eur J Trauma Emerg Surg 42(4):433–438

    Article  Google Scholar 

  18. Gordon TJ, Lidberg M (2015) Automated driving and autonomous functions on road vehicles. Veh Syst Dyn 53(7):958–994

    Article  Google Scholar 

  19. Hussain M, Bird JJ, Faria DR (2018) A study on cnn transfer learning for image classification. In UK workshop on computational intelligence. Springer, Cham. pp. 191-202

  20. Jia W, Xu S, Liang Z, Zhao Y, Min H, Li S, Yu Y (2021) Real-time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector. IET Image Process 15(14):3623–3637

    Article  Google Scholar 

  21. Jones P, Lucas K (2012) The social consequences of transport decision-making: clarifying concepts, synthesising knowledge and assessing implications. J Transp Geogr 21:4–16

    Article  Google Scholar 

  22. Kaarmukilan SP, Poddar S (2020) FPGA based deep learning models for object detection and recognition comparison of object detection comparison of object detection models using FPGA. In 2020 fourth international conference on computing methodologies and communication (ICCMC). IEEE. pp. 471-474

  23. Kawasaki N (1993) Parametric study of thermal and chemical nonequilibrium nozzle flow (Doctoral dissertation, MS thesis, Dept. Electron. Eng., Osaka Univ., Osaka, Japan)

  24. Langbein J, Putman RJ (2006) Deer–vehicle collisions project; Scotland, 2003–2005. Report to the Scottish Executive

  25. Li Z, Li Y, Yang Y, Guo R, Yang J, Yue J, Wang Y (2021) A high-precision detection method of hydroponic lettuce seedlings status based on improved faster RCNN. Comput Electron Agric 182:106054

    Article  Google Scholar 

  26. Lin Q, Ye G, Wang J, Liu H (2022) RoboFlow: a data-centric workflow management system for developing AI-enhanced robots. In conference on robot learning. PMLR. pp. 1789-1794

  27. Maeda H, Sekimoto Y, Seto T, Kashiyama T, Omata H (2018) Road damage detection using deep neural networks with images captured through a smartphone. arXiv preprint arXiv:1801.09454

  28. McGrath RG, MacMillan IC (2000) The entrepreneurial mindset: strategies for continuously creating opportunity in an age of uncertainty. Harvard Business Press, Brighton

  29. Mittal P, Singh R, Sharma A (2020) Deep learning-based object detection in low-altitude UAV datasets: a survey. Image Vis Comput 104:104046

    Article  Google Scholar 

  30. Morelle K, Lehaire F, Lejeune P (2013) Spatio-temporal patterns of wildlife-vehicle collisions in a region with a high-density road network. Nat Conserv 5:53–73

    Article  Google Scholar 

  31. Murray-Tuite P, Wolshon B (2013) Evacuation transportation modeling: an overview of research, development, and practice. Transp Res C Emerg Technol 27:25–45

    Article  Google Scholar 

  32. Nascimento JC, Marques JS (2006) Performance evaluation of object detection algorithms for video surveillance. IEEE Trans Multimedia 8(4):761–774

    Article  Google Scholar 

  33. Pouyanfar S, Sadiq S, Yan Y, Tian H, Tao Y, Reyes MP, Shyu ML, Chen SC, Iyengar SS (2018) A survey on deep learning: algorithms, techniques, and applications. ACM Comput Surv (CSUR) 51(5):1–36

    Article  Google Scholar 

  34. Prabhakar G, Kailath B, Natarajan S, Kumar R (2017) Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving. In 2017 IEEE region 10 symposium (TENSYMP). IEEE. (pp. 1-6)

  35. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779-788

  36. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. In NIPS, pp 1–9

  37. Ren Y, Zhu C, Xiao S (2018) Small object detection in optical remote sensing images via modified faster R-CNN. Appl Sci 8(5):813

    Article  Google Scholar 

  38. Road traffic injuries, (n.d.) https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries. Accessed 25 June 2022

  39. Sadeghniiat-Haghighi K, Yazdi Z (2015) Fatigue management in the workplace. Ind Psychiatry J 24(1):12–17

    Article  Google Scholar 

  40. Shao Z, Cai J, Wang Z (2017) Smart monitoring cameras driven intelligent processing to big surveillance video data. IEEE Trans Big Data 4(1):105–116

    Article  Google Scholar 

  41. Sharma SU, Shah DJ (2016) A practical animal detection and collision avoidance system using computer vision technique. IEEE Access 5:347–358

    Article  Google Scholar 

  42. Slobogin C (2002) Public privacy: camera surveillance of public places and the right to anonymity Miss lJ, 72, 213

  43. Soni AN (2018) Data center monitoring using an improved faster regional convolutional neural network. Int J Adv Res Electr Electron Instrum Eng 7(4):1849–1853. https://doi.org/10.15662/IJAREEIE.2018.0704058

    Article  Google Scholar 

  44. Southworth M (2005) Designing the walkable city. J Urban Plan Dev 131(4):246–257

    Article  Google Scholar 

  45. Speakman JR, Mitchell SE (2011) Caloric restriction. Mol Asp Med 32(3):159–221

    Article  Google Scholar 

  46. Summala H (1996) Accident risk and driver behaviour. Saf Sci 22(1–3):103–117

    Article  Google Scholar 

  47. Sun Z, Bebis G, Miller R (2004) On-road vehicle detection using optical sensors: a review. In proceedings. The 7th international IEEE conference on intelligent transportation systems (IEEE cat. No. 04TH8749). IEEE. pp. 585-590

  48. The times of India (2022) Road crashes rise by 10% in 2021 over 400 died in accidents in Gurugram. https://timesofindia.indiatimes.com/city/gurgaon/road-crashes-rise-by-10-in-2021-over-400-died-in-accidents-in-city/articleshow/88906699.cms. Accessed 20 Jan 2022

  49. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  50. Wang F, Ai Y, Zhang W (2022) Detection of early dangerous state in deep water of indoor swimming pool based on surveillance video. SIViP 16(1):29–37

    Article  Google Scholar 

  51. Wolf M, Weimerskirch A, Wollinger T (2007) State of the art: embedding security in vehicles. EURASIP J Embed Syst 2007:1–16

    Article  Google Scholar 

  52. Wu TH, Wang TW, Liu YQ (2021) Real-time vehicle and distance detection based on improved yolo v5 network. In 2021 3rd world symposium on artificial intelligence (WSAI). IEEE. pp. 24-28

  53. Yang Q, Koutsopoulos HN (1996) A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transp Res C Emerg Technol 4(3):113–129

    Article  Google Scholar 

  54. Yang Z, Yabansu YC, Al-Bahrani R, Liao WK, Choudhary AN, Kalidindi SR, Agrawal A (2018) Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets. Comput Mater Sci 151:278–287

    Article  Google Scholar 

  55. Zhao Y (2000) Mobile phone location determination and its impact on intelligent transportation systems. IEEE Trans Intell Transp Syst 1(1):55–64

    Article  Google Scholar 

  56. Zou Z, Shi Z, Guo Y, Ye J (2019) Object detection in 20 years: a survey. arXiv preprint arXiv:1905.05055

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Authors and Affiliations

  1. Department of Computer Engineering Technology, Guru Nanak Dev University, Amritsar, India

    Gursimran Singh Kahlon, Harnoor Singh, Munish Saini & Sandeep Kaur

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  1. Gursimran Singh Kahlon
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  2. Harnoor Singh
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  3. Munish Saini
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Correspondence to Gursimran Singh Kahlon.

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Kahlon, G.S., Singh, H., Saini, M. et al. An intelligent framework to detect and generate alert while cattle lying on road in dangerous states using surveillance videos. Multimed Tools Appl 82, 34589–34607 (2023). https://doi.org/10.1007/s11042-023-15019-3

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