The present research is focused on the object tracking domain based upon a new probabilistic estimation approach. This is realized through a generalization of the particle filter framework (PFF), in association with a neural network, namely an intelligence-based PFF (IPFF). In this idea, a number of appropriate features of mobile objects should first be considered for use in the process of IPFF realization to make the estimation and better performance. The applicability of the proposed approach has been considered though three separated scenarios, including non-stationary, stationary/non-stationary and finally stationary objects, as long as the standard mean shift object tracking approach is realized as a benchmark approach. The experimental results verify the approach performance improvement.
ErkmenBVuralRAKahramanN. (2013) A mixed mode neural network circuitry for object recognition application. Circuits, Systems, and Signal Processing32: 29–46.
2.
CaiYde FreitasNLittleJJ (2006) Robust visual tracking for multiple targets. In: Proceedings of the European conference on computer vision (ECCV), Graz, 7–13 May, pp. 107–118.
3.
Del BimboADiniF (2011) PFF-based visual tracking with a first order dynamic model and uncertainty adaptation. Computer Vision and Image Understanding115(6): 771–786.
4.
DrewTHorowitzTSVogelEK (2013) Swapping or dropping? Electrophysiological measures of difficulty during multiple object tracking original. Cognition126(2): 213–223.
5.
GustafssonFGunnarssonFBergmanN (2002) Particle filters for positioning, navigation, and tracking. IEEE Transactions on Signal Process50(2): 425–437.
6.
KristanM (2008) Tracking people in video data using probabilistic models. PhD thesis, Faculty of Electrical Engineering, University of Ljubljana.
7.
LopesNVCoutoPJurioA. (2013) Hierarchical fuzzy logic based approach for object trackingKnowledge-Based Systems54:255–268.
8.
LuoXWuYHuangY. (2011) Vehicle flow detection in real-time airborne traffic surveillance system. Transactions of the Institute of Measurement and Control33: 880–897.
9.
MazinanAHAmir LatifiA (2012a) Improvement of mean shift tracking performance using a convex kernel function and extracting motion information. Computers & Electrical Engineering38: 1595–1615.
10.
MazinanAHAmir LatifiA (2012b) Applying mean shift, motion information and Kalman filtering approaches to object tracking. ISA Transactions51: 485–497.
11.
MazinanAHAmir LatifiA (2012c) A new algorithm to rigid and non-rigid objects tracking in complex environments. International Journal of Advanced Manufacturing Technology64: 1643–1651.
12.
MazinanAHAmir LatifiAKazemiMF (2012) A knowledge-based objects tracking algorithm in color video using Kalman filter approach. IEEE International Conference on Information Retrieval & Knowledge Management, pp. 50–53.
13.
HossainMJDewanMAAAhnK. A linear time algorithm of computing Hausdorff distance for content-based image analysis. Circuits, Systems, and Signal Processing31(1): 389–399.
14.
PapoutsakisKEArgyrosAA (2013) Integrating tracking with fine object segmentation. Image and Vision Computing31(10): 771–785.
15.
RuiTZhangQZhouY. (2013) Object tracking using PFF in the wavelet subspace. Neurocomputing119: 125–130.
16.
YanZXuDTanM (2011) A fast and robust method for line detection based on image pyramid and Hough transform. Transactions of the Institute of Measurement and Control33: 971–984.
17.
YoonCCheonMParkM (2013) Object tracking from image sequences using adaptive models in fuzzy PFF. Information Sciences253: 74–99.
18.
ZhengSQiaoHZhangB. (2011) Tracking uncooperative person using a dynamic vision platform. Transactions of the Institute of Measurement and Control33: 823–845.
19.
AlliliMSZiouD (2006) Object contour tracking in videos by matching finite mixture models. IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS’06), p. 35.
20.
AlliliMSZiouD (2007) Active contours for video object tracking using region, boundary and shape information. Signal, Image and Video Processing1(2): 101–117.
21.
AsadiMDoreABeoldoA. (2007) Tracking by using dynamic shape model learning in the presence of occlusion. IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 230–235.
22.
BrasnettPAMihaylovaLSBullDR. (2007) Sequential Monte Carlo tracking by fusing multiple cues in video sequences. Image Vision Computing25: 1217–1227.
23.
DunnePMatuszewskiB (2011) Choice of similarity measure, likelihood function and parameters for histogram based PFF tracking in CCTV grey scale video. Image and Vision Computing29(2–3): 178–189.
24.
GavrilaDM (1999) The visual analysis of human movement: a survey. Computer Vision and Image Understanding73(1): 82–98.
25.
GeronimoDLopezAMSappaAD (201) Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence32(7): 1239–1258.
26.
HeikkilaMPietikainenM (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Transactions on Pattern Analysis and Machine Intelligence28(4): 657–662.
27.
HueCLe CadreJ-PPérezP (2002) Tracking multiple objects with particle filtering. IEEE Transactions on Aerospace and Electronic Systems38(3): 791–812.
28.
IsardMBlakeA (1998) Condensation-conditional density propagation for visual tracking. International Journal of Computer Vision29: 5–38.
29.
JingLChongZhaoHVadakkepatP (2011) Process noise identification based particle filter: an efficient method to track highly manoeuvring targets. IET Signal Processing, doi: 10.1049/iet-spr.2010.0025.
30.
JohanssonBWiklundJForssenP-E. (2009) Combining shadow detection and simulation for estimation of vehicle size and position. Pattern Recognition Letters30(8): 751–759.
31.
LeeJSandhuRTannenbaumA (2013) Particle filters and occlusion handling for rigid 2D–3D pose tracking. Computer Vision and Image Understanding117(8): 922–933.
32.
LiHuasheng (2002) Face tracking using skin-color model and Gabor wavelet. 6th International Conference on Signal Processing, pp. 837–840.
33.
LuWLOkumaKLittleJJ (2009) Tracking and recognizing actions of multiple hockey players using the boosted PFF. Image and Vision Computing27(1–2): 189–205.
34.
MaggioECavallaroA (2011) Video Tracking: Theory and Practice. New York: Wiley.
35.
McCaneBGalvinBNovisK (2002) Algorithmic fusion for more robust feature tracking. Journal of Computer Vision49: 79–89.
36.
MohedanoRdel-BlancoCRJaureguizarF. (2008) Robust 3D people tracking and positioning system in a semi-overlapped multi-camera environment. Image Processing, ICIP08, pp. 2656–2659.
37.
PerezPVermaakJBlakeA (2004) Data Fusion for Visual Tracking with Particles. Proceedings of the IEEE92(3): 495–513, 571.
38.
WangGLiangDWangY. (2007) Algorithm for tracking of fast motion objects with adaptive mean shift. Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007), vol. 1, pp. 359–363.
39.
YilmazAJavedOShahM (2006) Object tracking: a survey. ACM Computing Surveys38(4): 13.
