Skip to main content

Advertisement

SpringerLink
Log in

Integrated feature set using aggregate channel features and histogram of sparse codes for human detection

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

The scientific community witnessed revolutionary changes with algorithms and data sets aiming for precise human detection from images and videos which are largely driven by the quality of features extracted. Regardless of the labyrinth of the existing detectors, featured human detection with near accuracy from complicated real-time data sets remains a major challenge. Here we propose an improved feature set by merging the fast and accurate aggregate channel features (ACF) and the data specific dictionary learned histogram of sparse codes (HSC) for human detection. This integrated feature set efficiently fuses the first-order information from the histogram of oriented gradient channels embedded in the ACF detector and the data specific intelligence contained in the HSC channels. The proposed detector outperforms the state-of-the-art ACF detector in terms of miss rate and average precision on challenging datasets. It is worth to be noted that there is a decrease with the miss rate by a factor of 13% and 5% for INRIA and Caltech pedestrian datasets respectively in comparison with baseline detector. Along with the detection of more instances, our detector reduced the number of false positives compared to other existing detectors. Although further modifications are warranted, our proposed detector could produce a tangible and palpable response with human detection in the vast arena of computer vision.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Al-Hazaimeh OM, Al-Nawashi M, Saraee M (2018) Geometrical-based approach for robust human image detection. Multimed Tools Appl 78(6):7029–7053

    Article  Google Scholar 

  2. Bai X, Zhang T, Wang C, El-Latif AAA, Niu X (2013) A fully automatic player detection method based on one-class svm. IEICE Trans Inf Sys 96(2):387–391

    Article  Google Scholar 

  3. Benenson R, Mathias M, Timofte R, Van Gool L (2012) Pedestrian detection at 100 frames per second. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  4. Benenson R, Mathias M, Tuytelaars T, Van Gool L (2013) Seeking the strongest rigid detector. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  5. Benenson R, Omran M, Hosang J, Schiele B (2014) Ten years of pedestrian detection, what have we learned?. In: European conference on computer vision. Springer, pp 613–627

  6. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  7. Ding J, Wang Y, Geng W (2013) An hog-ct human detector with histogram-based search. Multimed Tools Appl 63(3):791–807

    Article  Google Scholar 

  8. Dollár P Piotr’s Computer Vision Matlab Toolbox (PMT). https://github.com/pdollar/toolbox

  9. Dollár P, Appel R, Belongie S, Perona P (2014) Fast feature pyramids for object detection. IEEE Trans Pattern Anal Mach Intell 36(8):1532–1545

    Article  Google Scholar 

  10. Dollár P, Tu Z, Perona P, Belongie S (2009) Integral channel features. In: Proceedings of the British machine vision conference. BMVC Press

  11. Dollar P, Wojek C, Schiele B, Perona P (2012) Pedestrian detection: an evaluation of the state of the art. IEEE Trans Patt Anal Mach Intell 34(4):743–761

    Article  Google Scholar 

  12. Fang Y, Sun L, Fu H, Wu T, Wang R, Dai B (2016) Learning deep compact channel features for object detection in traffic scenes. In: 2016 IEEE International conference on image processing (ICIP). IEEE, pp 1052–1056

  13. Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D (2010) Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell 32(9):1627–1645

    Article  Google Scholar 

  14. Gad R, Talha M, El-Latif AAA, Zorkany M, Ayman ES, Nawal EF, Muhammad G (2018) Iris recognition using multi-algorithmic approaches for cognitive internet of things (cIoT) framework. Futur Gener Comput Syst 89:178–191

    Article  Google Scholar 

  15. Jiang Y, Ma J (2015) Combination features and models for human detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 240–248

  16. Jing H, He X, Han Q, El-Latif AAA, Niu X (2014) Saliency detection based on integrated features. Neurocomputing 129:114–121

    Article  Google Scholar 

  17. Li A, Miao Z, Cen Y, Cen Y (2017) Anomaly detection using sparse reconstruction in crowded scenes. Multimed Tools Appl 76(24):26,249–26,271

    Article  Google Scholar 

  18. Li J, Liang X, Shen S, Xu T, Feng J, Yan S (2017) Scale-aware fast r-cnn for pedestrian detection. IEEE Transactions on Multimedia 20(4):985–996

    Google Scholar 

  19. Nigam S, Khare A (2015) Multiresolution approach for multiple human detection using moments and local binary patterns. Multimed Tools Appl 74(17):7037–7062

    Article  Google Scholar 

  20. Peng J, Li Q, El-Latif AAA, Niu X (2015) Linear discriminant multi-set canonical correlations analysis (ldmcca): an efficient approach for feature fusion of finger biometrics. Multimed Tools Appl 74(13):4469–4486

    Article  Google Scholar 

  21. Peng J, Wang N, El-Latif AAA, Li Q, Niu X (2012) Finger-vein verification using gabor filter and sift feature matching. In: 2012 Eighth international conference on intelligent information hiding and multimedia signal processing. IEEE, pp 45–48

  22. Ren X, Ramanan D (2013) Histograms of sparse codes for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3246–3253

  23. Sermanet P, Kavukcuoglu K, Chintala S, LeCun Y (2013) Pedestrian detection with unsupervised multi-stage feature learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3626–3633

  24. Viola P, Jones MJ, Snow D (2005) Detecting pedestrian using patterns of motion and appearance. Int J Comput Vis 63(2):153–161

    Article  Google Scholar 

  25. Wang X, Han TX, Yan S (2009) An hog-lbp human detector with partial occlusion handling. In: 2009 IEEE 12th international conference on computer vision. IEEE, pp 32–39

  26. Wang HH, Tu CW, Chiang CK (2019) Sparse representation for image classification via paired dictionary learning. Multimed Tools Appl 78(12):16,945–16,963

    Article  Google Scholar 

  27. Wang N, Li Q, El-Latif AAA, Peng J, Niu X (2014) An enhanced thermal face recognition method based on multiscale complex fusion for gabor coefficients. Multimed Tools Appl 72(3):2339–2358

    Article  Google Scholar 

  28. Xiong J, Tang Q, He X, Cai L, Wang F (2016) Tracking in multimedia data via robust reweighted local multi-task sparse representation for transportation surveillance. Multimed Tools Appl 75(24):17,531–17,552

    Article  Google Scholar 

  29. Yang BQ, Gu CC, Wu KJ, Zhang T, Guan XP (2017) Simultaneous dimensionality reduction and dictionary learning for sparse representation based classification. Multimed Tools Appl 76(6):8969–8990

    Article  Google Scholar 

  30. Zhang L, Lin L, Liang X, He K (2016) Is faster r-cnn doing well for pedestrian detection?. In: European conference on computer vision. Springer, pp 443–457

  31. Zhang S, Bauckhage C, Cremers AB (2014) Informed haar-like features improve pedestrian detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition

  32. Zhang T, El-Latif AAA, Wang N, Li Q, Niu X (2012) A new image segmentation method via fusing NCut eigenvectors maps. In: Fourth international conference on digital image processing (ICDIP 2012), vol 8334. SPIE, pp 593–596

  33. Zhang T, Han Q, El-Latif AAA, Bai X, Niu X (2013) 2-d cartoon character detection based on scalable-shape context and hough voting. Inf Technol J 12(12):2342–2349

    Article  Google Scholar 

  34. Zhao ZQ, Bian H, Hu D, Cheng W, Glotin H (2017) Pedestrian detection based on fast r-cnn and batch normalization. In: International conference on intelligent computing. Springer, pp 735–746

Download references

Author information

Authors and Affiliations

  1. College of Engineering Trivandrum, Kerala, India

    Blossom Treesa Bastian & Jiji C.V.

Authors
  1. Blossom Treesa Bastian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiji C.V.
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Blossom Treesa Bastian.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bastian, B.T., C.V., J. Integrated feature set using aggregate channel features and histogram of sparse codes for human detection. Multimed Tools Appl 79, 2931–2944 (2020). https://doi.org/10.1007/s11042-019-08498-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08498-w

Keywords

Search

Navigation