Object detection and tracking methods: A comprehensive review

Öz

Image processing is a method for reading, extracting, and processing important data in a digital image. One of the fundamental part of the image processing is collecting knowledge acquisition about recognized objects and environments in the image like human vision system. Many methods are developed to meet the needs of detection, identification, classification and tracking of objects in images. Especially, finding the target object in the images and following this object in the future time periods is frequently used in many applications. One of the main problems which makes object tracking and analysis difficult is tracking the object in a changing environments. Several effective methods are developed in order to solve these kinds of problems. In this paper, current and widely used methods for object tracking are discussed and those methods were examined with their strengths and weaknesses.

Anahtar Kelimeler

• Object tracking,background subtraction,object detection,performance analysis,object classification

Nesne tespit ve takip metotları: Kapsamlı bir derleme

Öz

Görüntü işleme dijital bir görüntü içerisindeki önemli bilgilerin okunması, çıkartılması ve işlenmesi için kullanılan bir yöntemdir. Görüntü içerisinde bulunan bir nesne ya da bir ortam hakkında insan görme sistemine benzer şekilde nitel bilgiler edinilmesi ve kullanılması görüntü işlemenin temel amaçlarındandır. Görüntülerde bulunan nesnelerin tespiti, tanımlanması, sınıflandırılması ve takibi gibi ihtiyaçları karşılayacak birçok yöntem geliştirilmiştir. Özellikle görüntülerdeki hedef nesnenin bulunması ve ileriki zaman dilimlerinde bu nesnenin kaybedilmemesi birçok alandaki uygulamalarda sıklıkla kullanılmaktadır. Takip edilecek nesnenin değişken bir ortam içinde bulunması nesne takibi ve analizini zorlaştıran temel problemdir. Bu problemleri çözmek ve nesnenin başarılı bir şekilde takip edilmesi için birçok farklı yöntem gelişmiştir. Bu çalışmada nesne takibi için güncel ve yaygın kullanılan yöntemler ele alınmıştır. İncelenen yöntemler güçlü/zayıf yönleri ile irdelenmiştir.

Anahtar Kelimeler

• Nesne izleme,arka plan çıkarma,nesne algılama,performans analizi,nesne sınıflandırması

Kaynakça

1. [1] Hjelmås E., Kee L.B., Face Detection: A Survey,Computer vision and image understanding, 83, 236–274., 2001
2. [2] Anagnostopoulos C.-N.E., Anagnostopoulos I.E., Psoroulas I.D., Loumos, V., Kayafas, E., License Plate Recognition From Still Images and Video Sequences: A Survey,IEEE Transactions on intelligent transportation systems, 9, 3:377–391, 2008
3. [3] Balaji S.R., KarthikeyanS.,A survey on moving object tracking using image processing, 2017 11th International Conference on Intelligent Systems and Control (ISCO), IEEE, pp. 469–474, 2017
4. [4] Wang D., Unsupervised video segmentation based on watersheds and temporal tracking,IEEE Transactions on Circuits and Systems for video Technology, 8.5:539–546, 1998
5. [5] Chen Y., Yang X., Zhong B., Pan S., et al., CNNTracker: Online discriminative object tracking via deep convolutional neural network,Applied Soft Computing, 38:1088–1098, 2016
6. [6] Luo W., Xing J., Milan A., Zhang X., Multiple Object Tracking: A Literature Review,arXiv Prepr. arXiv1409.7618, 2014
7. [7] Risha K.P., Kumar A.C., Novel Method of Detecting Moving Object in Video,Procedia Technology, 24:1055–1060, 2016
8. [8] Avidan S., Support vector tracking,IEEE transactions on pattern analysis and machine intelligence, 26.8: 1064-1072, 2004

9. [9] Hinton G., Deng L., Yu D., Dahl G., Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups,IEEE Signal Processing Magazine, 29.6: 82-97, 2012
10. [10] Krizhevsky A., Sutskever I., Hinton G.E., ImageNet Classification with Deep Convolutional Neural Networks, In: Advances in neural information processing systems, p. 1097-1105,2012
11. [11] Szegedy C., Liu W., Jia Y., Sermanet P., Going Deeper With Convolutions, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015
12. [12] Taigman, Y., Yang, M., Ranzato, M., Wolf, L., DeepFace: Closing the gap to human-level performance in face verification, In: Proceedings of the IEEE conference on computer vision and pattern recognition, p. 1701-1708, 2014
13. [13] Wu Y., Lim J., Yang M.-H., Online Object Tracking: A Benchmark, In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2411-2418, 2013
14. [14] Karasulu B., Videolardaki Hareketli Nesnelerin Tespit Ve Takibi İçin Uyarlanabilir Arkaplan Çıkarımı Yaklaşımı Tabanlı Bir Sistem,Uludağ Üniversitesi Mühendislik-Mimarlık Fakültesi Dergis, 18, 2013
15. [15] Shaikh S.H., Saeed K., Chaki N.,Moving Object Detection Approaches, Challenges and Object Tracking, In: Moving Object Detection Using Background Subtraction. Springer International Publishing, p. 5-14, 2014
16. [16] Aldhaheri A.R., Edirisinghe E.A., Detection and Classification of a Moving Object in a Video Stream, In: Proc. of the Intl. Conf. on Advances in Computing and Information Technology-ACIT, 2014.
17. [17] Hardas A., Vibha M., Moving Object Detection using Background Subtraction Shadow Removal and Post Processing,Int. J. Comput. Appl., 975–8887, 2015
18. [18] Li G., Wang Y., Shu W., Real-Time Moving Object Detection for Video Monitoring Systems, In: Intelligent Information Technology Application, 2008. IITA'08. Second International Symposium on, IEEE, pp. 163–166, 2008
19. [19] Martin C., Background Subtraction Using Running Gaussian Average: a Color Channel Comparison,In: Seminar aus Bildverarbeitung und Mustererkennung, 2014
20. [20] Manipriya S., Mala C., Mathew S., Performance Analysis of Spatial Color Information for Object Detection Using Background Subtraction,IERI Procedia, 10:63–69, 2014
21. [21] Stauffer, C., GrimsonW.E.L.,Adaptive background mixture models for real-time tracking, Proceedings,1999. IEEE Computer Society Conference on, IEEE, pp. 246–252, 1999
22. [22] Doyle D.D., Jennings A.L., Black J.T., Optical flow background estimation for real-time pan/tilt camera object tracking,Measurement, 48:195–207, 2014
23. [23] Tiwari, M., Singhai, R., A Review of Detection and Tracking of Object from Image and Video Sequences,International Journal of Computational Intelligence Research, 13, 973–1873, 2017
24. [24] Chate M., Amudha S., Gohokar V., Object Detection and tracking in Video Sequences,ACEEE International Journal on signal & Image processing, 3, 2012,
25. [25] Mohan A.S., Resmi R., Video image processing for moving object detection and segmentation using background subtraction, In: Computational Systems and Communications (ICCSC), 2014 First International Conference on,IEEE, pp. 288–292, 2014
26. [26] Haritaoglu I., Harwood D., Davis L.S., W/sup 4/: real-time surveillance of people and their activities,IEEE Transactions on pattern analysis and machine intelligence, 22, 809–830, 2000
27. [27] Zhiqiang W., Xiaopeng J., Peng W., Real-time moving object detection for video monitoring systems,Journal of Systems Engineering and Electronics,17, 731–736, 2006
28. [28] Zhang T., Liu Z., Lian X., Wang X., Study on moving-objects detection technique in video surveillance system, Chinese Control and Decision Conference, IEEE, pp. 2375–2380, 2010
29. [29] Krishna M.T.G., Ravishankar M., Babu D.R.R., Automatic detection and tracking of moving objects in complex environments for video surveillance applications, In: Electronics Computer Technology (ICECT), 2011 3rd International Conference on,IEEE, pp. 234–238, 2011
30. [30] Due Trier., Jain A.K., Taxt T., Feature extraction methods for character recognition-A survey, PatternRecognition, 29, 641–662, 1996.
31. [31] Fan L., Wang Z., Cail B., Tao C., A survey on multiple object tracking algorithm, In: Information and Automation (ICIA), 2016 IEEE International Conference on, IEEE, pp. 1855–1862, 2016
32. [32] Javed O., Shah M., Tracking and object classification for automated surveillance, In: European Conference on Computer Vision. Springer, Berlin, Heidelberg, pp. 343-357, 2002
33. [33] Hu, W., Tan, T., Wang, L., Maybank, S., A Survey on Visual Surveillance of Object Motion and Behaviors, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 34.3: 334-352, 2004
34. [34] Talu M.F., Nesne Takip Yöntemlerinin Sınıflandırılması,İstanbul Ticaret Üniversitesi Fen Bilim. Derg., 18, 45–63, 2010
35. [35] Yilmaz A., Javed O., Shah M., Object Tracking: A Survey,ACM computing surveys., 38, 2006
36. [36] Parekh H.S., Thakore D.G., Jaliya U.K., A survey on object detection and tracking methods, International Journal of Innovative Research in Computer and Communication Engineering, 2.2: 2970-2979. 2014
37. [37] Bagherpour P., Cheraghi S.A., Mokji M.B.M., Upper Body Tracking Using KLT and Kalman Filter, Procedia Computer Science, 13, 185–191, 2012
38. [38] Wang, N., Yeung, D.-Y., Learning a deep compact image representation for visual tracking, Advances in neural information processing systems., p. 809-817, 2013.
39. [39] Zhou X, Xie L, Zhang P, Zhang Y. An ensemble of deep neural networks for object tracking, In: Image Processing (ICIP), 2014 IEEE International Conference on, IEEE, p. 843-847, 2014
40. [40] Zhang D., Maei H., Wang X., Wang Y.-F., Deep Reinforcement Learning for Visual Object Tracking in Videos,arXiv Prepr. arXiv1701.08936, 2017
41. [41] Girshick R., Donahue J., Darrell T., Malik J., 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, 2014
42. [42] Behrendt K., Novak L., Botros R., A deep learning approach to traffic lights: Detection, tracking, and classification, In: Robotics and Automation (ICRA), 2017 IEEE International Conference on, pp. 1370–1377, 2017
43. [43] Gordon D., Farhadi A., Fox D., Re3: Real-Time Recurrent Regression Networks for Object Tracking, arXiv preprint arXiv:1705.06368, 2017.
44. [44] Bae S.-H., Yoon K.-J., Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017