Accurate Movement Detection of Artificially Intelligent Security Objects
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A number of intelligent applications demand multiple item detection and tracking as vital elements. Although object search links the identified object through a series of frames, object detection pinpoints the thing's location in a scene. Over the last few decades, a wide range of approaches has been created, which may be divided into 2D and stereo-based 3D techniques. When used in limited circumstances, most of these strategies yield trustworthy findings. These limiting presumptions are used to determine the number of complex elements that object detection and tracking naturally entail. Environmental factors, object appearance, flow density, backdrop colour intensity information, the amount of time an object is present in the scene, object occlusion, a scene's maximum number of things, etc., are among the most often held presumptions. In real-time applications, these approaches' dependability is not assured. A modern surveillance system needs reliable object identification and tracking in an open area.
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References
-
Chau DP, Bremond F, Thonnat M. Object Tracking in Videos: Approaches and Issues. The International Workshop Rencontres UNS- UD (RUNSUD, Danang, Vietnam; 2013.
Google Scholar
1
-
Dalal N, Triggs B. Histograms of oriented gradients for human detection. In The International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA, June 2005: 886-893.
Google Scholar
2
-
Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In The Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), April 2003: pp. 511-518.
Google Scholar
3
-
Faradji F, Rezaie AH, Ziaratban M. A morphological-based license plate location. In Proceedings of IEEE International Conference on Image Processing, 2007: pp. 57-60.
Google Scholar
4
-
Zheng K, Zhao YX, Gu J, Hu QM. License plate detection using Haar- like features and histogram of oriented gradients. In Proceedings of IEEE International Symposium on Industrial Electronics, 2012: pp. 1502-1505.
Google Scholar
5
-
Wu HHP, Chen HH, Wu RJ, Shen DF. License plate extraction in low resolution video. In Proceedings of the 18th International Conference on Pattern Recognition, 2006: pp. 824?827.
Google Scholar
6
-
Kwon J, Lee KM, Park FC. Visual tracking via geometric particle filtering on the affine group with optimal importance functions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009: pp. 991-998.
Google Scholar
7
-
Wang N, Yeung D. Learning a deep compact image representation for visual tracking. Proceedings of 27th Annual Conference on Neural Information Processing Systems (NIPS), 2013.
Google Scholar
8
-
Zhao J, Li Z. Particle filter based on Particle Swarm Optimization resampling for vision tracking. Elsevier Expert Syst. Appl., 2010; 37(12): 910-8914.
Google Scholar
9
-
Zhang B, Tian W, Jin Z. Robust appearance-guided particle filter for object tracking with occlusion analysis. Elsevier Int. J. Electron. Commun, 2008; 62(1): 24-32.
Google Scholar
10
-
Qin R, Liao S, Lei Z, Li SZ. Moving cast shadow removal based on local descriptors. ICPR, 2010.
Google Scholar
11
-
Martel-Brisson N, Zaccarin A. Learning and removing cast shadows through a multi distribution approach. TPAMI, 2007; 29(7): 1133-1146.
Google Scholar
12
-
Martel-Brisson N, Zaccarin A. Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation. CVPR, 2008.
Google Scholar
13
-
Lee W, Lee G, Ban S-W, Jung I, Lee M. Intelligent video surveillance system using dynamic saliency map and boosted Gaussian mixture model. In International conference on neural information processing. Springer; 2011: pp. 557-564.
Google Scholar
14
-
Wang M, Qiao H, Zhang B. A new algorithm for robust pedestrian tracking based on manifold learning and feature selection. IEEE Transactions on Intelligent Transportation Systems, 2011; 12: 1195-1208.
Google Scholar
15
-
Zhang L, Li Y, Nevatia R. Global data association for multi-object tracking using network flows. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008: pp. 1-8.
Google Scholar
16
-
Doucet A, de Freitas N, Gordon N. Sequential Monte Carlo Methods in Practice. Springer; 2001.
Google Scholar
17
-
Seo H, Milanfar P. Training-free, generic object detection using locally adaptive regression kernels. IEEE Trans. Pattern Anal. Mach. Intell., 2010; 32(9): 1688?1704.
Google Scholar
18
-
Henriques J, Caseiro R, Batista J. Globally optimal solution to multi- object tracking with merged measurements. In Proceedings / IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision, 2011.
Google Scholar
19
-
Huang Y, Essa I. Tracking multiple objects through occlusions. CVPR, 2005.
Google Scholar
20
-
Babenko, Yang M-H, Belongie S. Visual tracking with online multiple instance learning. CVPR, 2009.
Google Scholar
21
-
Dinh TB, Vo N, Medioni G. Context Tracker: Exploring Supporters and Distracters in Unconstrained Environments. CVPR, 2011.
Google Scholar
22
-
Mei X, Ling H. Robust Visual Tracking and Vehicle Classification via Sparse Representation. TPAMI, 2011; 33(11): 2259-2272.
Google Scholar
23
-
Yoshinaga S, Shimada A, Nagahara H, Taniguchi R, Background model based on intensity change similarity among pixels. In 19th Japan- Korea Joint Workshop on Frontiers of CV, Jan. 2013, pp. 276-280.
Google Scholar
24
-
Park D, Byun H. Object-wise multilayer background ordering for pubic area surveillance. In IEEE Int. Conf. AVSS, Sep. 2009: pp. 484-489.
Google Scholar
25
-
Evangelio RH, P?tzold M, Sikora T. Splitting gaussians in mixture models. In Proc. 9th IEEE Int. Conf. Advanced Video and Signal-Based Surveillance, Sep. 2012: pp. 300-305.
Google Scholar
26
-
Porikli F, Tuzel O. Bayesian background modeling for foreground detection. In Proc. ACM VSSN, Nov. 2005: pp. 55-58.
Google Scholar
27
-
Strauss O, Sidib D, Puech W. Quasi-continuous histogram based motion detection. Technical Report, LE2I; 2012.
Google Scholar
28
-
Lee DS. Effective gaussian mixture learning for video background subtraction. IEEE Trans. Patt. Anal. Mach. Intell., 2005; 27(5): 827-832.
Google Scholar
29
-
Adam A, Rivlin E, Shimshoni I. Robust fragments-based tracking using the integral histogram. In Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recog., Jun. 2006; 1: 798-805.
Google Scholar
30
-
Mei X, Ling H. Robust visual tracking using l1 minimization. In Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Oct. 2009: pp. 1436-1443.
Google Scholar
31
-
Ross D, Lim J, Lin R, Yang M. Incremental learning for robust visual tracking. Int. J. Comput. Vis., 2008; 77(1): 125-141.
Google Scholar
32
-
Grabner H, Grabner M, Bischof H. Real-time tracking via on-line boosting. In Proc. British Mach. Vis. Conf. (BMVC), 2006; 1: 47-56.
Google Scholar
33
-
Babenko B, Yang M-H, Belongie S. Visual tracking with online multiple instance learning. In Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), Jun. 2009: pp. 983-990.
Google Scholar
34
-
Kwon J, Lee KM. Visual tracking decomposition. In Proc. IEEE Conf.Comput. Vis. Pattern Recog. (CVPR), Jun. 2010: pp. 1269-1276.
Google Scholar
35
-
Kwon J, Lee KM. Tracking by sampling trackers. In Proceedings of IEEE Int. Conf. Comput. Vis. (ICCV), Nov. 2011: pp. 1195-1202.
Google Scholar
36
-
Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection. IEEE Trans. Pattern Anal.Mach. Intell., 2012; 34(7): 1409-1422.
Google Scholar
37
-
Zhang T, Ghanem B, Liu S, Ahuja N, Robust visual tracking via multi-task sparse learning. In Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), Jun. 2012: pp. 2042-2049.
Google Scholar
38
-
Zhang K, Zhang L, Yang M-H. Real-time compressive tracking. In Proc. Eur. Conf. Comput. Vis. (ECCV), 2012: pp. 864-877.
Google Scholar
39
-
Hare S, Saffari A, Torr PHS. Struck: Structured output tracking with kernels. In Proc. IEEE Int. Conf. Comput. Vis. (ICCV), Nov. 2011: pp. 263-270.
Google Scholar
40
-
Yao R, Shi Q, Shen C, Zhang Y, van den Hengel A. Part-based visual tracking with online latent structural learning. In IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), Jun. 2013: pp. 2363?2370.
Google Scholar
41
-
Fei M, Li J, Liu H. Visual tracking based on improved foreground detection and perceptual hashing. Neurocomputing, 2015; 152: 413-428.
Google Scholar
42
-
Fei M, Li J, Shao L, Ju Z, Ouyang G. Robust Visual Tracking Based on Improved Perceptual Hashing for Robot Vision. Int?l Conference on Intelligent Robotics and Applications, Springer; 2015: pp. 331-340.
Google Scholar
43
-
Kumar S, Yadav JS. Video object extraction and its tracking using background subtraction in complex environments, Recent Trends in Engineering and Material Sciences. Elsevier: Perspectives in Science, 2016; 8: 317-322.
Google Scholar
44
-
Huang S, Jiang S, Zhu X. Multi-object tracking via discriminative appearance modeling: Computer Vision and Image Understanding. Elsevier, 2016; 153: 77-87.
Google Scholar
45
-
Li Z, Gao S, Nai K. Robust object tracking based on adaptive templates matching via the fusion of multiple features, J. Vis. Commun. Image R, 2017; 44: 1-20.
Google Scholar
46
-
Tian S, Yuan F, Xia G-S. Multi-object tracking with inter-feedback between detection and tracking. Neurocomputing, 2016; 171: 768-780.
Google Scholar
47
-
Wang R, Sang N, Wang R, Jiang L. Detection and tracking strategy for license plate detection in video. Optik, 2014; 125: 2283-2288.
Google Scholar
48
-
Ahmadi K, Salari E. Small dim object tracking using frequency and spatial domain information. Pattern Recognition, 2016; 58: 227-234.
Google Scholar
49
-
Sardari F, Moghaddam ME. A hybrid occlusion free object tracking method using particle filter and modified galaxy based search meta-heuristic algorithm. Applied Soft Computing, 2017; 50: 280-299.
Google Scholar
50
-
Elafi I, Jedra M, Zahid N. Unsupervised detection and tracking of moving objects for video surveillance applications. Pattern Recognition Letters, 2016; 84: 70-77.
Google Scholar
51
-
Huerta I, Holte MB, Moeslund TB, Gonz?lez J. Chromatic shadow detection and tracking for moving foreground segmentation. Image and Vision Computing, 2015; 41: 42-53.
Google Scholar
52
-
Lee G, Mallipeddi R, Lee M. Trajectory-based vehicle tracking at low frame rates. Expert Systems with Applications, 2017; 80: 46-57.
Google Scholar
53
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