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Intelligent video analysis for enhanced pedestrian detection by hybrid metaheuristic approach

  • K. R. Sri PreethaaEmail author
  • A. Sabari
Methodologies and Application
  • 20 Downloads

Abstract

Intelligent video analytics for pedestrian detection plays a vital role for enhanced and effective surveillance system. Since smart city projects are gaining momentum in most of the countries nowadays, enhanced pedestrian detection plays a vital role in the field of security and surveillance. Various classification models were in existence for detecting the pedestrians which suffers from variety of challenges like illumination, pedestrian outfits, gestures, occlusion, lighting, etc., that affects the accuracy of detection. A strong feature vector describing the pedestrian is developed to enhance the accuracy of detection. In this paper, a novel hybrid metaheuristic pedestrian detection (HMPD) approach is proposed to enhance the accuracy of the classifier. HMPD extracts the working principles of support vector machine and genetic algorithm. The proposed model is trained using a set of human and non-human images. The accuracy of the proposed model is tested with benchmarking video data available at VISOR repository. The result clearly shows that HMPD approach produces the maximum accuracy than any traditional approaches. HMPD approach can further be applied in other domains for enhanced security and surveillance.

Keywords

Pedestrian detection HOG filter Video analytics Metaheuristic 

Notes

Compliance with ethical standards

Conflict of interest

All authors state that there is no conflict of interest.

References

  1. Andriluka M, Roth S, Schiele B (2008) People-tracking-by-detection and people-detection-by-tracking. In: IEEE Conference on computer vision and pattern recognition, pp 1–8Google Scholar
  2. Badler N, Smoliar S (1979) Digital representations of human movement. ACM Comput Surv 11(1):19–38CrossRefGoogle Scholar
  3. Badler N, Phillips C, Webber B (1993) Simulating humans. Oxford University Press, OxfordzbMATHGoogle Scholar
  4. Benenson R, Mathias M, Timofte R, Van Gool L (2012) Pedestrian detection at 100 frames per second. In: 2012 IEEE Conference on computer vision and pattern recognition (CVPR), pp 2903–2910Google Scholar
  5. Bhadra T, Sonar J, Sarmah A, Kumar CJ (2015) Pedestrian detection: a survey of methodologies, techniques and current advancements. Int J Sci Res Eng Technol (IJSRET) 4(1):31–36. ISSN 2278–0882Google Scholar
  6. Chang FL, Liu X, Wang H-J (2007) Target tracking algorithm based on Meanshift and Kalman filter. JisuanjiGongchengyuYingyong (Comput Eng Appl) 43(12):50–52Google Scholar
  7. Chen C-C, Lin H-H, Chen OT-C (2011) Tracking and counting people in visual surveillance systems. In: IEEE ICASSP, pp 1425–1428Google Scholar
  8. Cosma C, Brehar R, Nedevschi S (2013) Pedestrians detection using a cascade of LBP and HOG classifiers. In: ICCP, pp 69–75Google Scholar
  9. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE Computer society conference on computer vision and pattern recognition (CVPR’05), pp 886–893Google Scholar
  10. de Poortere V, Cant J, Van den Bosch B, de Prins J, Fransens F, Van Gool L (2002) Efficient pedestrian detection: a test case for SVM based categorization. In: Workshop on cognitive vision, vol. 1. pp 19–20. http://www.vision.ethz.ch/cogvis02/
  11. Dhankhar P, Sahu N (2014) Edge based human face detection using Matlab. In: CSE ITM University Gurgaon-Haryana, proceedings of IRF international conference, 16th February 2014Google Scholar
  12. Dollár P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761CrossRefGoogle Scholar
  13. Enzweiler M, Gavrila DM (2009) Monocular pedestrian detection: survey and experiments. IEEE Trans Pattern Anal Mach Intell 31(12):2179–2195CrossRefGoogle Scholar
  14. Felzenszwalb P, Huttenlocher D (2000) Efficient matching of pictorial structures. In: CVPR, Hilton Head Island, South Carolina, USA, pp 66–75Google Scholar
  15. Gavrila DM (1999) The visual analysis of human movement: a survey. CVIU 73(1):82–98zbMATHGoogle Scholar
  16. Gavrila DMM, Giebel J (2002) Shape-based pedestrian detection and tracking. In: Intelligent vehicle symposium, pp 8–14Google Scholar
  17. Gavrila DM, Philomin V (1999) Real-time object detection for smart vehicles. In: CVPR, Fort Collins, Colorado, USA, pp 87–93Google Scholar
  18. Gavrila DM, Giebel J, Munder S (2004) Vision-based pedestrian detection: the protector system. In: Proceedings of the IEEE intelligent vehicles symposium, Parma, ItalyGoogle Scholar
  19. Geronimo D, Sappa A, Lopez A, Ponsa D (2007) Adaptive image sampling and windows classification for on-board pedestrian detection. In: Proceedings of the 5th international conference on computer vision systemsGoogle Scholar
  20. Ioffe S, Forsyth DA (2001) Probabilistic methods for finding people. IJCV 43(1):45–68CrossRefGoogle Scholar
  21. Jiang J, Xiong H (2012) Fast pedestrian detection based on HOG-PCA and gentle AdaBoost. In: International CSSS, 2012, pp 1819–1822Google Scholar
  22. Jiang Z, Huynh DQ, Moran W, Challa S (2013) Combining background subtraction and temporal persistency in pedestrian detection from static videos. In: 20th IEEE ICIP, pp 4141–4145Google Scholar
  23. Jiang Y, Tong G, Yin H, Xiong N (2019) A pedestrian detection method based on genetic algorithm for optimize XGBoost training parameters. IEEE Spec Sect Data Min Internet Things 7:118310–118321Google Scholar
  24. Ke Y, Sukthankar R (2004) PCA-SIFT: a more distinctive representation for local image descriptors. In: CVPR, Washington, DC, USA, pp 66–75Google Scholar
  25. Li J, Huang L, Liu C (2011) An efficient self-learning people counting system. In: First Asian conference on ACPRGoogle Scholar
  26. Li B, Li Y, Tian B, Zhu F, Xiong G, Wang K (2013) Part-based pedestrian detection using grammar model and ABM-HoG features. In: Proceedings of IEEE international conference on vehicular electronics and safety, pp 78–83Google Scholar
  27. Liang J, Ye Q, Chen J, Jiao J (2012) Evaluation of local feature descriptor and their combination for pedestrian representation. In: International conference on pattern recognition, pp 2496–2499Google Scholar
  28. Mikolajczyk K, Schmid C, Zisserman A (2004) Human detection based on a probabilistic assembly of robust part detectors. In: 8th ECCV, Prague, Czech Republic, vol I, pp 69–81Google Scholar
  29. Min K, Son H, Choe Y, Kim Y-G (2013) Real-time pedestrian detection based on a hierarchical two-stage support vector machine. In: IEEE 8th ICIEA, pp 114–119Google Scholar
  30. Mogelmose A, Prioletti A, Trivedi MM, Broggi A, Moeslund TB (2012), Two-stage part-based pedestrian detection. In: 15th International IEEE conference on intelligent transportation systems, pp 73–77Google Scholar
  31. Qu J, Liu Z (2012) Non-background HOG for pedestrian video detection. In: 8th International conference on natural computation, pp 535–539Google Scholar
  32. Rahulamathavan Y, Phan RCW, Chambers JA, Parish DJ (2012) Facial expression recognition in the encrypted domain based on local fisher discriminant analysis. IEEE Trans Affect Comput 4(1):83–92CrossRefGoogle Scholar
  33. Rajagopalan AN, Kumar K, Karlekar J, Manivasakan R, Patil M, Desai U, Poonacha P, Chaudhuri S (1998) Finding faces in photographs. In: Proceedings in sixth IEEE international conference on computer vision, pp 640–645Google Scholar
  34. Roncancio H, Hernandes AC, Becker M (2012) Vision-based system for pedestrian recognition using a tuned SVM classifier. In: WEA, pp 1–6Google Scholar
  35. Ronfard R, Schmid C, Triggs B (2002) Learning to parse pictures of people. In: The 7th ECCV, Copenhagen, Denmark, vol IV, pp 700–714CrossRefGoogle Scholar
  36. Schneiderman H, Kanade T (2004) Object detection using the statistics of parts. IJCV 56(3):151–177CrossRefGoogle Scholar
  37. Setu TA, Rahman MM (2016) Human face classification using genetic algorithm. Int J Adv Comput Sci Appl (IJACSA) 7(9):312–317Google Scholar
  38. Shih P, Liu C (2005) Face detection using distribution-based distance and support vector machine. In: Proceeding of 6th international conference computing intelligent, multimedia applications (ICCIMA), Las Vegas, NV, USA, pp 327–332Google Scholar
  39. Sledevie T, Serackis A, Plonis D (2018) FPGA-based selected object tracking using LBP, HOG and motion detection. In: Proceedings of IEEE 6th workshop on Advances in information, electronic and electrical engineering (AIEEE), pp 1–5Google Scholar
  40. Solichin A, Harjoko A, Putra AE (2014) A survey of pedestrian detection in video. Int J Adv Comput Sci Appl (IJACSA) 5(10):41–47Google Scholar
  41. Subba R et al (2015) Genetic algorithm based human face recognition. In: Proceeding of international conference on advances in communication, network, and computing, pp 417–426Google Scholar
  42. Viola P, Jones MJ (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 511–518Google Scholar
  43. Viola P, Jones MJ, Snow D (2003) Detecting pedestrians using patterns of motion and appearance. In: The 9th ICCV, Nice, France, vol 1, pp 734–741Google Scholar
  44. Wang X, Han TX, Yan S (2009) An HOG-LBP human detector with partial occlusion handling. In: Proceedings, IEEE 12th international conference on computer vision, Kyoto, Japan, pp 32–39Google Scholar
  45. Wu J, Yang S, Zhang L (2011) Pedestrian detection based on improved HOG feature and robust adaptive boosting algorithm. In: 4th International Congress on image signal processing, pp 1535–1539Google Scholar
  46. Xie Y, Pei M, Yu G, Song X, Jia Y (2011) Tracking pedestrians with incremental learned intensity and contour templates for PTZ camera visual surveillance. In: International conference on multimedia and expo, pp 5–10Google Scholar
  47. Yang DB, Stanford U, View M, Guibas LJ (2003) Counting people in crowds with a real-time network of simple image sensors. In: ICCVGoogle Scholar
  48. Yang Y, Liu W, Wang Y, Cai Y (2012) Research on the algorithm of pedestrian recognition in front of the vehicle based on SVM. In: 11th International symposium on distributed computing and applications to business, engineering and science, pp 396–400Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2020

Authors and Affiliations

  1. 1.Department of CSEKPR Institute of Engineering and TechnologyCoimbatoreIndia
  2. 2.Department of ITK S Rangasamy College of TechnologyTiruchengodeIndia

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