Qualitative Performance Metrics For Object Tracking Algorithms

Harihara Santosh Dadi*, Gopala Krishna Mohan Pillutla**, Abdul-Karim***
* Associate Professor, Aditya Institute of Technology and Management, Hyderabad, India.
** Professor, Institute of Aeronautical College of Engineering, Hyderabad, India.
*** Professor, Department of Electronics and Communication Engineering, JNT University, Telangana, India.
Periodicity:October - December'2017
DOI : https://doi.org/10.26634/jip.4.4.14164

Abstract

In this paper, Qualitative metrics for object tracking algorithms is introduced. The two algorithms are subjectively analyzed, and four different types of datasets based on occlusion strength, contrast, illumination, and clutter are taken. Each dataset is of simple and hard type. Gaussian Mixture Model (GMM) and Weighted Running Window Background (WRWB) model based GMM are the two human tracking algorithms taken for subjective analysis. The algorithms are verified for both easy and hard type datasets. The performance metrics show that these parameters are best to analyze any object tracking algorithms subjectively. Precision and recall parameters are considered for subjective analysis.

Keywords

Gaussian Mixture Model, Object Tracking, Weighted Running Window Background.

How to Cite this Article?

Dadi, H.S., Pillutla, G.K.M. and Makkena, M.L. (2017). Qualitative Performance Metrics for Object Tracking Algorithms. i-manager’s Journal on Image Processing, 4(4), 1-15. https://doi.org/10.26634/jip.4.4.14164

References

[1]. Bashir, F., & Porikli, F. (2006). Performance evaluation of object detection and tracking systems. In Proceedings 9th IEEE International Workshop on PETS (pp. 7-14). IEEE.
[2]. Benfold, B., & Reid, I. (2011, June). Stable multi-target tracking in real-time surveillance video. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on (pp. 3457-3464). IEEE.
[3]. Bernardin, K., & Stiefelhagen, R. (2008). Evaluating multiple object tracking performance: The CLEAR MOT metrics. Journal on Image and Video Processing, 2008, 1-10.
[4]. Birchfield, S. T., & Rangarajan, S. (2005). Spatiograms versus histograms for region-based tracking. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on (Vol. 2, pp. 1158- 1163). IEEE.
[5]. Brown, L. M., Senior, A. W., Tian, Y. L., Connell, J., Hampapur, A., Shu, C. F., ... & Lu, M. (2005). Performance evaluation of sur veillance systems under var ying conditions. In Proceedings of IEEE PETS Workshop (pp. 1- 8). IEEE.
[6]. Chau, D. P., Brémond, F., Thonnat, M., & Corvée, E. (2011). Robust mobile object tracking based on multiple feature similarity and trajectory filtering. In VISAPP11 (pp. 569-574).
[7]. Chu, D. M., & Smeulders, A. W. (2010). Thirteen hard cases in visual tracking. In Advanced Video and Signal Based Surveillance (AVSS), 2010 Seventh IEEE International Conference on IEEE (pp. 103-110). IEEE.
[8]. Comaniciu, D., Ramesh, V., & Meer, P. (2003). Kernelbased object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 564-577.
[9]. Corvee, E., & Bremond, F. (2011). Haar like and LBP based features for face, head and people detection in video sequences. In International Workshop on Behaviour Analysis and Video Understanding (ICVS 2011) (p. 10).
[10]. Dadi, H. S., Pillutla, G. K. H., & Makkena, M. L. (2017a). Human tracking using weighted running window background model based GMM. i-manager's Journal on Software Engineering, 12(2), 44-54.
[11]. Dadi, H. S., Pillutla, G. K. M., & Latha Makkena, M. (2017b). Human tracking under severe occlusions. imanager's Journal on Software Engineering, 12(1), 29-37.
[12]. Hager, G. D., Dewan, M., & Stewart, C. V. (2004). Multiple kernel tracking with SSD. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on (Vol. 1, pp. 1-10). IEEE.
[13]. Lee, D. S. (2005). Effective Gaussian mixture learning for video background subtraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(5), 827- 832.
[14]. Li, Y., Huang, C., & Nevatia, R. (2009). Learning to associate: Hybridboosted multi-target tracker for crowded scene. In Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on IEEE (pp. 2953-2960). IEEE.
[15]. Nghiem, A. T., Bremond, F., Thonnat, M., & Ma, R. (2007). A new evaluation approach for video processing algorithms. In Motion and Video Computing, 2007. WMVC'07. IEEE Workshop on (pp. 1-8). IEEE.
[16]. Paalanen, P., Kamarainen, J. K., Ilonen, J., & Kälviäinen, H. (2006). Feature representation and discrimination based on Gaussian Mixture Model probability densities-practices and algorithms. Pattern Recognition, 39(7), 1346-1358.
[17]. Rafael C. Gonzalez, Richard E. Woods, & Steven L. Eddins. (2011). Digital Image Processing using MATLAB®, nd 2 edition TMH Publications, New Delhi.
[18]. Ristani, E., Solera, F., Zou, R., Cucchiara, R., & Tomasi, C. (2016). Performance measures and a data set for multi-target, multi-camera tracking. In European Conference on Computer Vision (pp. 17-35). Springer, Cham.
[19]. Rosenholtz, R., Li, Y., & Nakano, L. (2007). Measuring visual clutter. Journal of Vision, 7(2), 17-17.
[20]. Sankaranarayanan, S., Bremond, F., & Tax, D. (2012). Qualitative evaluation of detection and tracking performance. In Advanced Video and Signal-Based Surveillance (AVSS), 2012 IEEE Ninth International Conference on (pp. 362-367). IEEE.
[21]. Santosh, D. H., & Mohan, P. K. (2014a). Tracking manifold objects in motion using Gaussian mixture model and blob analysis. In IEEE International Conference on Convergence of Technology-2014 (pp. 1-7). IEEE.
[22]. Santosh, D. H., & Mohan, P. K. (2014b). Multiple objects tracking using extended Kalman Filter, GMM and Mean Shift algorithm - A comparative study. In Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on (pp. 1484- 1488). IEEE.
[23]. Santosh, D. H., & Mohan, P. K. (2015). Multiple human tracking and prediction under severe occlusions using GMM and Kalman filter. IJAER, 10(11), 29385-29404.
[24]. Wang, Y., Zhang, J., Wu, L., & Zhou, Z. (2010). Mean shift tracking algorithm based on multi-feature space and grey model. Journal of Computational Information Systems, 6(11), 3731-3739.
[25]. Wu, H., Sankaranarayanan, A. C., & Chellappa, R. (2010). Online empirical evaluation of tracking algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1443-1458.
[26]. Yao, J., & Odobez, J. M. (2008, October). Fast Human Detection from Videos using Covariance Features. In The Eighth International Workshop on Visual Surveillance-VS2008.
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