Sweta PANIGRAHI, Surya Narayana Raju UNDI

Scale-invariant histogram of oriented gradients: novel approach for pedestrian detection in multiresolution image dataset

This paper proposes a scale-invariant histogram of oriented gradients (SI-HOG) for pedestrian detection. Most of the algorithms for pedestrian detection use the HOG as the basic feature and combine other features with the HOG to form the feature set, which is usually applied with a support vector machine (SVM). Hence, the HOG feature is the most efficient and fundamental feature for pedestrian detection. However, the HOG feature produces feature vectors of different lengths for different image resolutions; thus, the feature vectors are incomparable for the SVM. The proposed method forms a scale-space pyramid wherein the histogram bin is calculated. Thus, the gradient information from all the scales is encapsulated in a single fixed-length feature vector. The proposed method is also combined with color and texture features. The proposed approach is tested on three established benchmark pedestrian datasets: INRIA, NICTA, and Daimler. An improvement of ≥4.5% in the miss rate is achieved for all the three datasets considered. We also show that the SI-HOG can be applied to multiresolution datasets for which the HOG feature cannot be applied. Additionally, the MapReduce model is used to obtain the same outcome. The results indicate that the proposed approach outperforms the pedestrian-detection methods considered in this work.

PDF

___

[1] Gavrila DM. A bayesian, exemplar-based approach to hierarchical shape matching. IEEE Transactions on Pattern Analysis & Machine Intelligence 2007; 29 (8): 1408-1421. doi: 10.1109/TPAMI.2007.1062
[2] Papageorgiou CP. A trainable system for object detection in images and video sequences. PhD, Massachusetts Institute of Technology, Cambridge, MA, USA, 1991.
[3] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: International Conference on Computer Vision & Pattern Recognition; San Diego, USA; 2005. pp. 886–893.
[4] Tuzel O, Porikli F, Meer P. Pedestrian detection via classification on riemannian manifolds. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008; 30 (10): 1713-1727. doi: 10.1109/TPAMI.2008.75
[5] Leibe B, Leonardis A, Schiele B. Robust object detection with interleaved categorization and segmentation. International Journal of Computer Vision 2008; 77 (1-3): 259-289. doi: 10.1007/s11263-007-0095-3
[6] Lowe DG. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2004; 60 (2): 91-110. doi: 10.1023/B:VISI.0000029664.99615.94
[7] Satpathy A, Jiang X, Eng HL. Human detection by quadratic classification on subspace of extended histogram of gradients. IEEE Transactions on Image Processing 2013; 23 (1): 287-297. doi: 10.1109/TIP.2013.2264677
[8] Nigam S, Khare A. Multiresolution approach for multiple human detection using moments and local binary patterns. Multimedia Tools and Applications 2015; 74 (17): 7037-7062. doi: 10.1007/s11042-014-1951-0
[9] Overett G, Petersson L, Brewer N, Andersson L, Pettersson N. A new pedestrian dataset for supervised learning. In: IEEE Intelligent Vehicles Symposium; Eindhoven, Netherlands; 2008. pp. 373-378.
[10] Yan J, Zhang X, Lei Z, Liao S, Li SZ. Robust multi-resolution pedestrian detection in traffic scenes. In: IEEE Conference on Computer Vision and Pattern Recognition; Portland, USA; 2013. pp. 3033-3040.
[11] Felzenszwalb PF, Girshick RB, McAllester D, Ramanan D. Object detection with discriminatively trained partbased models. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009; 32 (9): 1627-1645. doi: 10.1109/TPAMI.2009.167
[12] Dollar P, Wojek C, Schiele B, Perona P. Pedestrian Detection: An evaluation of the state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence 2012; 34 (4): 743-761. doi: 10.1109/TPAMI.2011.155
[13] Hurney P, Waldron P, Morgan F, Jones E, Glavin M. Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors. IET Intelligent Transport Systems 2014; 9 (1): 75-85. doi: 10.1049/ietits.2013.0163
[14] Bilal M, Hanif MS. High performance real-time pedestrian detection using light weight features and fast cascaded kernel SVM classification. Journal of Signal Processing Systems 2019; 91 (2): 117-29. doi: 10.1007/s11265-018-1374- 7
[15] Lahmyed R, El Ansari M, Ellahyani A. A new thermal infrared and visible spectrum images-based pedestrian detection system. Multimedia Tools and Applications 2019; 78 (12): 15861-85. doi: 10.1007/s11042-018-6974-5
[16] Bastian BT, Jiji CV. Integrated feature set using aggregate channel features and histogram of sparse codes for human detection. Multimedia Tools and Applications 2020; 79 (3): 2931-44. doi: 10.1007/s11042-019-08498-w
[17] Kumar K, Mishra RK. A heuristic SVM based pedestrian detection approach employing shape and texture descriptors. Multimedia Tools and Applications 2020; 79: 21389–21408. doi: 10.1007/s11042-020-08864-z
[18] Zhang X, Shangguan H, Ning A, Wang A, Zhang J et al. Pedestrian detection with EDGE features of color image and HOG on depth images. Automatic Control and Computer Sciences 2020; 54 (2): 168–178. doi: 10.3103/S0146411620020108
[19] Park D, Ramanan D, Fowlkes C. Multiresolution models for object detection. In: European conference on computer vision; Crete, Greece; 2010. pp. 241-254.
[20] Xu Z, Li B, Yuan Y, Dang A. Beta R-CNN: looking into pedestrian detection from another perspective. In: Advances in Neural Information Processing Systems 33; Vancouver, Canada; 2020.
[21] Wu J, Zhou C, Zhang Q, Yang M, Yuan J. Self-mimic learning for small-scale pedestrian detection. In: Proceedings of the 28th ACM International Conference on Multimedia; Seattle, WA, USA; 2020. pp. 2012-2020.
[22] Song X, Zhao K, Zhang WSCH, Guo J. Progressive refinement network for occluded pedestrian detection. In: Proceedings European Conference on Computer Vision; Glasgow, UK; 2020. p. 9.
[23] Lin C, Lu J, Wang G, Zhou J. Graininess-aware deep feature learning for robust pedestrian detection. IEEE transactions on image processing 2020; 29: 3820-3834. doi: 10.1109/TIP.2020.2966371.
[24] Karg M, Scharfenberger C. Deep learning-based pedestrian detection for automated driving: achievements and future challenges. In: Pedrycz W, Chen SM (editors). Development and Analysis of Deep Learning Architectures 1st ed. Cham, Switzerland: Springer Nature, 2020, pp. 117-143.
[25] Zhu Q, Yeh MC, Cheng KT, Avidan S. Fast human detection using a cascade of histograms of oriented gradients. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition; New York, USA; 2006. pp. 1491-1498.
[26] Zhao Y, Zhang Y, Cheng R, Wei D, Li G. An Enhanced Histogram of Oriented Gradients for Pedestrian Detection. In: Ljubo V (editor). IEEE Intelligent Transportation Systems Magazine 7th vol. Nathan Campus, Australia: IEEE, 2015, pp. 29-38.
[27] Huang J, Kumar SR, Mitra M, Zhu WJ, Zabih R. Image indexing using color correlograms. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition; San Juan, USA; 1997. pp. 762-768.
[28] Chun YD, Kim NC, Jang IH. Content based image retrieval using multiresolution color and texture features. IEEE Transactions on Multimedia 2008; 10 (6): 1073-1084. doi: 10.1109/TMM.2008.2001357
[29] Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence 2002; 24 (7): 971-987. doi: 10.1109/TPAMI.2002.1017623
[30] Heikkilä M, Pietikäinen M, Schmid C. Description of interest regions with center-symmetric Local Binary Patterns. In: Kalra PK, Peleg S (editors). Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Berlin, Heidelberg, Germany: Springer, 2006, pp. 58-69.
[31] Verma M, Raman B, Murala S. Local extrema co-occurrence pattern for color and texture image retrieval. Neurocomputing 2015; 165: 255-269. doi: 10.1016/j.neucom.2015.03.015
[32] Bhunia AK, Bhattacharyya A, Banerjee P, Roy PP, Murala S. A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern. Pattern Analysis and Applications 2020; 23: 703–723. doi: 10.1007/s10044-019-00827-x
[33] Zhang B, Gao Y, Zhao S, Liu J. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing 2010; 19 (2): 533-544. doi: 10.1109/TIP.2009.2035882
[34] Murala S, Maheshwari RP, Balasubramanian R. Local tetra patterns: a new feature descriptor for content based image retrieval. IEEE Transactions on Image Processing 2012; 21 (5): 2874-2886. doi: 10.1109/TIP.2012.2188809
[35] Wang Y, Zhao Y, Chen Y. Texture classification using rotation invariant models on integrated local binary pattern and zernike moments. EURASIP Journal on Advances in Signal Processing 2014; (1): 182-192. doi: 0.1186/1687- 6180-2014-182
[36] Vapnik V. The Nature of Statistical Learning Theory. Berlin, Heidelberg, Germany: Springer, 1995.
[37] Mohan A, Papageorgiou C, Poggio T. Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001; 23 (4): 349-361. doi: 10.1109/34.917571
[38] Munder S, Gavrila DM. An experimental study on pedestrian classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 2006; 28 (11): 1863-1868. doi: 10.1109/TPAMI.2006.217
[39] Maji S, Berg AC, Malik J. Efficient classification for additive kernel SVMs. IEEE Transactions on Pattern Analysis and Machine Intelligence 2013; 35 (1): 66-77. doi: 10.1109/TPAMI.2012.62
[40] Mayer-Schönberger V, Cukier K. Big Data: A Revolution that will Transform How We Live, Work, and Think. London, UK: John Murray, 2013.
[41] Uddin MF, Gupta N. Seven V’s of big data understanding big data to extract value. In: Zone 1 Conference of the American Society for Engineering Education; Bridgeport, CT, USA; 2014. pp. 1-5.
[42] Zhang L, Rui Y. Image search-from thousands to billions in 20 years. ACM Transactions on Multimedia Computing, Communications, and Applications 2013; 9 (1s): 1-20. doi: 10.1145/2490823
[43] Godil A, Bostelman R, Shackleford W, Hong T, Shneier M. Performance Metrics for Evaluating Object and Human Detection and Tracking Systems. Gaithersburg, MD, USA: National Institute of Standards and Technology (NIST) Interagency/Internal Report (NISTIR) - 7972, 2014.
[44] Powers DM. Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation. Journal of Machine Learning Technologies 2011; 2 (1): 37-63.
[45] Fawcett T. An introduction to ROC analysis. Pattern recognition letters 2006; 27 (8): 861-874. doi: 10.1016/j.patrec.2005.10.010
[46] Martin A, Doddington G, Kamm T, Ordowski M, Przybocki M. The DET Curve in Assessment of Detection Task Performance. Gaithersburg, MD, USA: National Institute of Standards and Technology (NIST), 1997.
[47] Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association 1937; 32 (200): 675-701. doi: 10.1080/01621459.1937.10503522
[48] Iman RL, Davenport JM. Approximations of the critical region of the fbietkan statistic. Communications in Statistics-Theory and Methods 1980; 9 (6): 571-595. doi: 10.1080/03610928008827904
[49] Toprak T, Belenlioglu B, Aydın B, Guzelis C, Selver MA. Conditional weighted ensemble of transferred models for camera based onboard pedestrian detection in railway driver support systems. IEEE Transactions on Vehicular Technology 2020; 69 (5): 5041-5054. doi: 10.1109/TVT.2020.2983825
[50] Kuncheva LI, Whitaker CJ. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine learning 2003; 51 (2): 181-207. doi: 10.1023/A:1022859003006