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Recent advances on multicue object tracking: a survey

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Abstract

The performance of single cue object tracking algorithms may degrade due to complex nature of visual world and environment challenges. In recent past, multicue object tracking methods using single or multiple sensors such as vision, thermal, infrared, laser, radar, audio, and RFID are explored to a great extent. It was acknowledged that combining multiple orthogonal cues enhance tracking performance over single cue methods. The aim of this paper is to categorize multicue tracking methods into single-modal and multi-modal and to list out new trends in this field via investigation of representative work. The categorized works are also tabulated in order to give detailed overview of latest advancement. The person tracking datasets are analyzed and their statistical parameters are tabulated. The tracking performance measures are also categorized depending upon availability of ground truth data. Our review gauges the gap between reported work and future demands for object tracking.

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References

  • Ahmed M, Tawab A, Abdelhalim MB, Habib SED (2014) Efficient multi-feature PSO for fast gray level object tracking. Appl Soft Comput 14:317–337

    Article  Google Scholar 

  • Airouche M, Bentabet L, Zelmat M, Gao G (2012) Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework. Mach Vis Appl 23:999–1010

    Article  Google Scholar 

  • Ambardekar A, Niclescu M, Dascalu S (2009) Ground truth verification Tool (GTVT) for video surveillance system. In: ACHI’09 second international conferences on advances in computer–human interactions

  • Andriluka M, Roth S, Schiele B (2010) Monocular 3D pose estimation and tracking by detection. In: IEEE conference on computer vision and pattern recognition (CVPR 2010), USA

  • Baltieri D, Vezzani R, Cucchiara R (2011) 3DPes: 3D people dataset for surveillance and forensics. In: Proceedings of international ACM workshop MA3HO, Scottsdale, AZ, USA, pp 59–64

  • Bashir F, Porikli F (2006) Performance evaluation of object detection and tracking systems. In: IEEE international workshop on performance evaluation of tracking and surveillance (PETS)

  • Bernardin K, Stiefelhagen R (2008) Evaluating multiple object tracking performance: the clear MOT metrics. EURASIP J IVP 2008(1):246–309

    Google Scholar 

  • Birchfield S (1998) Elliptical head tracking using intensity gradients and color histogram. In: Proceedings of IEEE conference on computer vision and pattern reorganization, pp 232–237

  • Black J, Ellis T, Rosin P (2003) A novel method for video tracking performance evaluation. In: Joint IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance (VS-PETS), pp 125–132

  • Blunsden SJ, Fisher RB (2010) The BEHAVE video dataset: ground truth video for multi-person behavior classification. Ann BMVA 4:1–12

    Google Scholar 

  • Brasnett P, Mihaylova L, Bull D, Canagarajah N (2007) Sequential Monte Carlo tracking by fusing multiple cues in video sequences. Image Vis Comput 25(8):1217–1227

    Article  Google Scholar 

  • Brasnett P, Mihaylova L, Canagarajah N, Bull D (2005) Particle filtering with multiple cues for object tracking invideo sequences. In: Proceedings of SPIE’s 17th annual symposium on electronic imaging, science and technology, vol 5685, pp 430–441

  • Brown LM, Senior WA, Tian Y-l, Connell J, Hampapur A, Shu C-F, Merkl H, Max L, (2005) Performance evaluation of surveillance systems under varying conditions. In: IEEE internationl workshop on performance evaluation of tracking and surveillance, Colorado

  • Cannons K, Gryn J, Wildes R (2010) Visual tracking using a pixel wise spatio-temporal oriented energy representation. In: Proceedings of the11th European conference on computer vision, pp 511–524

  • CANTATA. http://www.multitel.be/cantata/

  • Chakravarty P, Jarvis R (2006) Panoramic vision and laser range finder fusion for multiple person tracking. In: Proceedings of IEEE/RSJ international conference on intelligent robots and systems, pp 2949–2954

  • Chau DP, Bremond F, Thonnat M (2009) Online evaluation of tracking algorithm performance. In: The 3rd international conference on imaging for crime detection and prevention

  • Chen Y, Nguyen TV, Kankanhalli M, Yuan J, Shuicheng Yan, Meng Wang (2014) Audio matters in visual attention. IEEE Trans Circuits Syst Video Technol 24(11):1992–2003

    Article  Google Scholar 

  • Chen Y, Rui Y (2004) Real-time speaker tracking using particle filter sensor fusion. In: Proceedings of the IEEE, vol 92(3)

  • CHIL—computers in the human interaction loop. http://chil.server.de/

  • CLEAR: classification of events, activities and relationships. http://www.clear-evaluation.org, 2008

  • Collins R, Zhou X, Teh SK (2005) An open source tracking test bed and evaluation web site. In: IEEE international workshop on performance evaluation of tracking and surveillance (PETS2005)

  • Conaire CO, O’ Connor NE, Smeaton A (2008) Thermo-visual feature fusion for object tracking using multiple spatiogram trackers. Mach Vis Appl 19:483–494

    Article  Google Scholar 

  • Congxia Dai, Yunfei Zheng, Xin Li (2007) Pedestrian detection and tracking in infrared imagery using shape and appearance. Comput Vis Image Underst 106:288–299

    Article  Google Scholar 

  • Cui J, Zha H, Zhao H, Shibasaki R (2008) Multi-modal tracking of people using laser scanners and video camera. Image Vis Comput 26:240–252

  • Davis J, Keck M (2005) A two-stage approach to person detection in thermal imagery. In: Proceedings of IEEE workshop on applications of computer vision

  • Davis J, Sharma V (2007) Background-subtraction using contour-based fusion of thermal and visible imagery. IEEE OTCBVS WS Ser Bench Comput Vis Image Underst 106(2–3):162–182

    Article  Google Scholar 

  • Denman S, Fookes C, Sridharan S, Lakemond R (2009) Dynamic performance measures for object tracking systems. In: Proceeding of advanced video and signal based surveillance, IEEE

  • Dou JF, Jianxun Li (2014) Robust visual tracking based on interactive multiple model particle filter by integrating multiple cues. Neurocomputing 135:118–129

    Article  Google Scholar 

  • Dou J, Li J (2014) Robust visual tracking base on adaptively multi-feature fusion and particle filter. Optik 125:1680–1686

    Article  Google Scholar 

  • EC Caviar project/IST 2001 37540, found at http://homepages.inf.ed.ac.uk/rbf/CAVIAR/

  • Erdem CE, Sankur B, Tekalp AM (2001a) Non-rigid object tracking using performance evaluation measures as feedback. In: Proceedings of IEEE internationl conference on computer vision and pattern recognition, pp II-323–II-330

  • Erdem CE, Tekalp AM, Sankur B (2001b) Metrics for performance evaluation of video object segmentation and tracking without ground truth. In: Proceedings of international conference on image processing, vol 2, pp 69–72

  • Erdem E, Dubuisson S, Bloch I (2012) Visual tracking by fusing multiple cues with context-sensitive reliabilities. Pattern Reorgan 45:1948–1959

    Article  MATH  Google Scholar 

  • Ess A, Leibe B, van Gool L (2007) Depth and appearance for mobile scene analysis. In: Proceedings of ICCV

  • Fleuret F, Berclaz J, Lengagne R, Fua P (2008) Multi-camera people tracking with a probabilistic occupancy map. IEEE Trans Pattern Anal Mach Intell 30(2):267–282

    Article  Google Scholar 

  • Francesc MN, Sanfeliu A, Samaras D (2005) Integration of conditionally dependent object features for robust figure/background segmentation. In: Proceedings of IEEE international conference on computer vision (ICCV), vol 2, pp 1713–1720

  • Francesc MN, Sanfeliu A, Samaras D (2008) Dependent multiple cue integration for robust tracking. IEEE Trans Pattern Anal Mach Intell 30(4):670–685

    Article  Google Scholar 

  • Gavrila DM, Munder S (2007) Multicue pedestrian detection and tracking from a moving vehicle. Int J Comput Vis 73:41–59

    Article  Google Scholar 

  • Gedik OS, Alatan A (2013) 3-D rigid body tracking using vision and depth sensors. IEEE Trans Cybern 43(5):1395–1405

    Article  Google Scholar 

  • Germa T, Lerasle F, Quadah N, Cadenat V (2010) Vision and RFID data fusion for tracking people in crowds by a mobile robot. Comput Vis Image Underst 114:641–651

    Article  Google Scholar 

  • Han J, Pauwels EJ, de Zeeuw Paul M, de With Peter HN (2012) Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment. IEEE Trans Consum Electron 58(2):1318–1334

    Google Scholar 

  • Han J, Shao L, Xu D, Shotton J (2013) Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans Cybern 43(5):1318–1334

  • Hong Liu, Ze Yu, Hongbin Zha, Yuexian Zou, Lin Zhang (2009) Robust human tracking based on multicue integration and mean shift. Pattern Recognit Lett 30:827–837

    Article  Google Scholar 

  • Hong Lu, Zou WL, Li HS, Zhang Y, Fei SM (2015) Edge and color contexts based object representation and tracking. Optik 126:148–152

    Article  Google Scholar 

  • HTS. http://www.ces.clemson.edu/_stb/research/headtracker/seq/

  • Hu J, Su TM, Cheng CC, Liu, WH, Wu TI (2002) A self-calibrated speaker tracking system using both audio and video data. In: Proceedings of the 2002 IEEE international conference on control applications

  • INO’s Video Analytics Dataset, found at URLwww.ino.ca/en/video-analytics-dataset/

  • Jacobs RA (2002) What determines visual cue reliability? Trends Cogn Sci 6(8):345–350

    Article  Google Scholar 

  • Karasulu B, Korukoglu S (2011) A software for performance evaluation and comparison of people detection and tracking methods in video processing. Multimed Tools Appl 55(3):677–723

    Article  Google Scholar 

  • Kasturi R, Goldgof D, Soundararajan P, Manohar V, Boonstra M, Korzhova V (2005) Performance evaluation protocol for text, face, hands, person and vehicle detection & tracking in video analysis and content extraction (VACEII). Technical report, University of South Florida

  • Kılıç V, Barnard M, Wang W, Kittler J (2015) Audio assisted robust visual tracking with adaptive particle filtering. IEEE Trans Multimed 17(2):186–200

    Article  Google Scholar 

  • Kim DY, Moongu Jeon (2014) Data fusion of radar and image measurements for multi-object tracking via Kalman filtering. Inf Sci 278:641–652

    Article  MathSciNet  Google Scholar 

  • Kinect camera. http://www.xbox.com/en-US/kinect/default.htm

  • Klein DA BoBoT—Bonn benchmark on tracking. http://www.iai.uni-bonn.de/~kleind/tracking/index.htm

  • Klein DA, Schulz D, Frintrop S, Cremers AB (2010) Adaptive real-time video-tracking for arbitrary objects. In: Proceedings of IEEE IROS, Taipei, Taiwan, pp 772–777

  • Kong S, Heo BAJ, Paik J, Abidi M (2005) Recent advances in visual and infrared face recognitio—a review. Comput Vis Image Underst 97(1):103–135

    Article  Google Scholar 

  • Kumar P, Brooks MJ, Dick A (2007) Adaptive multiple object tracking using colour and segmentation cues. In: Asian conference on computer vision (ACCV 2007), Tokyo, Japan, Lecture notes in computer science, Springer, vol 4844, pp 853–863

  • Kumar P, Dick A, Brooks MJ (2008) Integrated Bayesian multicue tracker for objects observed from moving cameras. In: International conference on image and vision computing, New Zealand

  • Kumar P, Dogancay K (2011) Fusion of colour and facial features for person matching in a camera network. In: Seventh international conference on intelligent sensors, sensor networks and information processing, Adelaide

  • Kumar S, Marks TK, Jones M (2014) Improving person tracking using an inexpensive thermal infrared sensor. In: IEEE conference on computer vision and pattern recognition workshops

  • Kwon J, Lee KM (2009) Tracking of a non-rigid object via patch-based dynamic appearance modelling and adaptive basin hopping Monte Carlo sampling. In: Proceedings of IEEE CVPR, Miami, FL, USA

  • Lathoud G, Odobez JM, Gatica-Perez D (2004) AV16.3: an audio-visual corpus for speaker localization and tracking. In: Proceedings of the MLMI’04 workshop

  • Leichter I, Lindenbaum M, Rivlin E (2014) The cues in “dependent multiple cue integration for robust tracking” are independent. IEEE Trans Pattern Anal Mach Intell 36(3):620–621

  • Li T, Sun S, Sattar TP, Corchado JM (2014a) Fight sample degeneracy and impoverishment in particle filters: a review of intelligent approaches. Expert Syst Appl 41:3944–3954

  • Li Ying, Liang S, Bai B, Feng D (2014b) Detecting and tracking dim small targets in infrared image sequences under complex backgrounds. Multimed Tools Appl 71:1179–1199

  • Li Z, He S, Hashem M (2014c) Robust object tracking via multi-feature adaptive fusion based on stability: contrast analysis. Vis Comput 31:1432–2315

  • Lim YS, Jong S, Kim M (2007) Particle filter algorithm for single speaker tracking with audio-video data fusion. In: 16th IEEE international conference on robot and human interactive communication

  • Liu J, Liu Y, Zhang G, Zhu P, Chen YQ (2015) Detecting and tracking people in real time with RGB-D camera. Pattern Recognit Lett 53:16–23

    Article  Google Scholar 

  • Loy G, Fletcher L, Apostoloff N, Zelinsky A (2002) Adaptive fusion architecture for target tracking. In: IEEE international conference on automatic face and gesture recognition (FGR)

  • Maggio E, Smeraldi F, Cavallaro A (2007) Adaptive multi-feature tracking in a particle filtering framework. IEEE Trans Circuits Syst Video Technol 17(10):1–12

    Article  Google Scholar 

  • Maggio E, Cavallaro A (2005) Multi-part target representation for color tracking. In: Proceedings of IEEE international conference on image processing, Genoa, Italy, vol 1, pp 729–732

  • Megherbi N, Ambellouis S, Colot O, Cabestaing F (2005) Joint audio–video people tracking using belief theory. In: Proceeding of IEEE conference on advanced video and signal based surveillance

  • Milan A, Schindler K, Roth S (2013) Challenges of ground truth evaluation of multi-target tracking. In: Proceeding of IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 735–742

  • Motai Y, Jha SK, Kruse D (2012) Human tracking from a mobile agent: optical flow and Kalman filter arbitration. Signal Process Image Commun 27:83–95

    Article  Google Scholar 

  • MOTINAS project: courtesy of EPSRC funded MOTINAS project (EP/D033772/1). www.eecs.qmul.ac.uk/~andrea/avss2007_d.html

  • Munaro M, Basso F, Menegatti E (2012) People tracking within groups with RGB-D data. In: Proceedings of the international conference on intelligent robots and systems (IROS), Algarve (Portugal)

  • Munoz-salinas R, Miguel GS, Carnicer RM (2008) Adaptive multi-modal stereo people tracking without background modelling. J Vis Commun Image Represent 19:75–91

    Article  Google Scholar 

  • Murphy RR (1996) Biological and cognitive foundations of intelligent sensor fusion. IEEE Trans Syst Man Cybern A Syst Hum 26(1):42–51

    Article  Google Scholar 

  • Nghiem AT, Bremond F, Thonnat M, Valentin V, ETISEO (2007) Performance evaluation for video surveillance systems. In: IEEE international conference on advanced video and signal based surveillance (AVSS), London (UK)

  • Nickel K, Gehrig T, Ekenel H, Stiefelhagen R, McDonough J (2005) A joint particle filter for audio-visual speaker tracking. In: International conference on multimodal interfaces (ICMI05). Torento, Italy, pp 61–68

  • Nickel K, Stiefelhagen R (2008) Dynamic Integration of generalized cues for person tracking. ECCV 2008, Part IV, LNCS 5305:514–526

    Google Scholar 

  • Patino L, Ferryman J (2014) PETS 2014: dataset and challenge. In: 11th IEEE international conference on advanced video and signal based surveillance (AVSS)

  • Perez P, Vermaak J, Blake A (2004) Data fusion for visual tracking with particles. In: Proceedings of the IEEE, vol 92(3)

  • PETS 2006. http://www.cvg.reading.ac.uk/PETS2006/data.html

  • PETS 2006 IEEE international workshop on performance evaluation of tracking and surveillance. http://www.pets2006.net/

  • PETS 2014 benchmark data. Multi camera sequences containing activity with different threat and difficulty levels. Pets2014.net

  • Pingali G, Segen J (1996) Performance evaluation of people tracking systems. In: Proceedings of IEEE workshop on applications of computer vision, pp 33–38

  • Polycom Worldwide [Online]. http://www.polycom.com/

  • Portmann J, Lynen S, Chli M, Siegwart R (2014) People detection and tracking from aerial thermal views. In: Proceedings of IEEE conference on robotics and automation

  • SanMiguel JC, Cavallaro A, Martinez JM (2012) Adaptive on-line performance evaluation of video trackers. IEEE Trans Image Process 21(5):1828–1837

    Article  MathSciNet  Google Scholar 

  • Scheutz M, McRaven J, Cserey Gy (2004) Fast, reliable, adaptive bimodal people tracking for indoor environments. In: IEEE conference on robots system

  • Schlogl T, Beleznai C, Winter M, Bischof H (2004) Performance evaluation metrics for motion detection and tracking. In: Proceedings of the pattern recognition, 17th international conference on ICPR’04, 4: IEEE Computer Society, Washington, DC, USA, pp 519–522

  • Schulz D (2006) A probabilistic exemplar approach to combine laser and vision for person tracking. In: Robotics: science and systems (RSS), Philadelphia, USA

  • Senior A, Hampapur A, Ying-Li Tian, Brown L, Pankanti S, Bole R (2001) Appearance models for occlusion handling. In: IEEE international workshop on performance evaluation of tracking and surveillance, Kauai, HI

  • Shen C, Hengel AVD, Dick A (2003) Probabilistic multiple cue integration for particle filter based tracking. In: Sun C, Talbot H, Ourselin S, Adriansen T (eds) Proceedings of the VIIth digital image computing: techniques and applications

  • Smeulders AWM, Chu DM, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442–1468

    Article  Google Scholar 

  • Song X, Zhao H, Cui J, Shao X, Shibasaki R, Zha H (2013) An online system for multiple interacting targets tracking: fusion of laser and vision, tracking and learning. ACM Trans Intell Syst Technol 4(1):1–21

    Article  Google Scholar 

  • Song X, Cui J, Zhao H, Zha H (2008) Bayesian fusion of laser and vision for multiple people detection and tracking. In: Proceedings of IEEE international conference on instrumentation, control and information technology, pp 3014–3019

  • Spampinato C, Palazzo S, Giordano D (2012) Evaluation of tracking algorithms performance without ground truth data. In: Proceedings of 19th IEEE international conference on image processing (ICIP), Orlando, FL, pp 1345–1348

  • Spengler M, Schiele B (2003) Towards robust multicue integration for visual tracking. Mach Vis Appl 14(1):50–58

    Article  Google Scholar 

  • Spinello L, Triebel R, Siegwart R (2009) A trained system for Multimodal perception in urban environment. In: Proceedings of the IEEE ICRA, workshop on people detection and tracking

  • Stolkin R, Rees D, Talha M, Florescu I (2012) Bayesian fusion of thermal and visible spectra camera data for region based tracking with rapid background adaptation. In: Proceedings of IEEE international conference on multisensor fusion information and integration, pp 192–199

  • Strigel E, Meissner D, Seeliger F, Wilking B, Dietmayer K (2014) The Ko-PER intersection laser scanner and video dataset. In: IEEE 17th international conference on intelligent transportation systems (ITSC), pp 1900–1901

  • Strobel N, Spors S, Rabenstein R (2001) Joint audio-video object localization and tracking. IEEE Signal Process Mag 18:22–33

  • Sun Y, Bentabet L (2010) A particle filtering and DSmT based approach for conflict resolving in case of target tracking with multiple cues. J Math Imaging Vis 36:159–167

    Article  MathSciNet  Google Scholar 

  • Susperregi L, Martinez-Otzeta JM, Ansuategui A, Aitorlbarguren Sierra B (2013) RGB-D, laser and thermal sensor fusion for people following in a mobile robot. Int J Adv Robot Syst 100:1–9

    Article  Google Scholar 

  • Talantzis F, Pnevmatikakis A, Constantinides AG (2008) Audio-visual active speaker tracking in cluttered indoors environments. IEEE Trans Syst Man Cybern B Cybern 39(3):799–807

    Article  Google Scholar 

  • Talha M, Stolkin R (2012) Adaptive fusion of infra-red and visible spectra camera data for particle filter tracking of moving targets. In: Proceedings of IEEE sensors conference, pp 1–4

  • Talha M, Stolkin R (2014) Particle filter tracking of camouflaged targets by adaptive fusion of thermal and visible spectra camera data. IEEE Sens J 14(1):151–166

    Article  Google Scholar 

  • Thomaidis G, Manolis Tsgas, Lytrivis P, Karaseitanidis G, Amditis A (2013) Multiple hypothesis tracking for data association in vehicular networks. Inf Fusion 14:374–383

    Article  Google Scholar 

  • Torabi A, Masse G, Bilodeau GA (2012) An iterative integrated framework for thermal-visible image registration, sensor fusion and people tracking for video surveillance applications. Comput Vis Image Underst 116:210–221

    Article  Google Scholar 

  • Treptow A, Cielniak G, Duckett T (2005) Active people recognition using thermal and grey images on a mobile security robot. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS), Edmonton, Alberta

  • Triesch J, Malsburg CVD (2001) Democratic integration: self-organized integration of adaptive cues. Neural Comput 13:2049–2074

    Article  MATH  Google Scholar 

  • Utasi A, Benedek C (2012) A multi-view annotation tool for people detection evaluation. In: Workshop on visual interfaces for ground truth collection in computer vision applications, Capri, Italy

  • ViPER-GT, the ground truth authoring tool. http://vipertoolkit.sourceforge.net/docs/gt/

  • VIVID database. http://vision.cse.psu.edu/data/vividEval/datasets/datasets.html

  • Vondrick C, Patterson D, Ramanan D (2012) Efficiently scaling up crowd sourced video annotation. Int J Comput Vis (IJCV) 101:184–204

    Article  Google Scholar 

  • Walia GS, Rajiv K (2014) Human detection in video and images—a state-of-the-art survey. Int J Pattern Recognit Artif Intell 28(3):1–25

  • Walia GS, Rajiv K (2015) Robust object tracking based upon multi-cue integration for video surveillance. Multimed Tools Appl. doi:10.1007/s11042-015-2890-0

  • Walia Gurjit Singh, Kapoor Rajiv (2014) intelligent video target tracking using an evolutionary particle filter based upon improved cuckoo search. Expert Syst Appl 41:6315–6326

    Article  Google Scholar 

  • Wang JT, Chen DB, Chen HY, Yang JY (2012) On pedestrian detection and tracking in infrared videos. Pattern Recognit Lett 33(6):775–785

    Article  Google Scholar 

  • Wang Q, Fang J, Yuan Y (2014) Multicue based tracking. Neurocomputing 131:227–236

    Article  Google Scholar 

  • Wang H, David S (2006) Efficient visual tracking by probabilistic fusion of multiple cues. In: Proceedings of the 18th international conference on pattern recognition, pp 892–895

  • Wu H, Sankaranarayanan A, Chellappa R (2010) Online empirical evaluation of tracking algorithms. IEEE Trans Pattern Anal Mach Intell 32(8):1443–1458

    Article  Google Scholar 

  • Wu H, Zheng Q (2004) Self-evaluation for video tracking systems. Technical report, Department of Eletrical and Computer Engineering, Maryland University, College Park

  • Xu Y, Ye M, Zunhua Zhang X (2014) Locally adaptive combining color and depth for human body contour tracking using level set method. IET Comput Vis 8(4):316–328

    Article  Google Scholar 

  • Yang H, Shao L, Zheng F, Wang L, Song Z (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74(18):3823–3831

    Article  Google Scholar 

  • Yang X, Wang M, Tao D (2015) Robust visual tracking via multi-graph ranking. Neurocomputing 159:35–43

    Article  Google Scholar 

  • Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45

    Article  Google Scholar 

  • Yin M, Zhang J, Sun H, Gu W (2011) Multicue based camshaft guided particle filter tracking. Expert Syst Appl 38(5):6313–6318

    Article  Google Scholar 

  • Ying L, Zhang T, Changsheng Xu (2014) Multi-object tracking via MHT with multiple information fusion in surveillance video. Multimed Syst 21:313–326

    Article  Google Scholar 

  • Yin F, Makris D, Velastin SA (2007) Performance evaluation of object tracking algorithms. In: 10th IEEE international workshop on performance evaluation of tracking and surveillance, PETS 2007

  • Zeng F, Xuan Liu, Huang Z, Ji Y (2013) Kernel based multiple cue adaptive appearance model for robust real-time visual tracking. IEEE Signal Process Lett 20(11):1094–1097

    Article  Google Scholar 

  • Zhang M, Ming X, Yang J (2013a) Adaptive multicue based particle swarm optimization guided particle filter tracking in infrared videos. Neurocomputing 121:163–171

  • Zhang X, Hong Liu, Xiaohong Sun (2013b) Object tracking with an evolutionary particle filter based on self-adaptive multi-features fusion. Int J Adv Robot Syst 10:1–11

  • Zhang S, Yao H, Sun X, Lu X (2013c) Sparse coding based visual tracking: review and experiment comparison. Pattern Recognit 46(7):1772–1788

  • Zhang X, Li Wei, Xiuzi Ye, Maybank S (2015) Robust hand tracking via novel multicue integration. Neurocomputing 157:296–305

    Article  Google Scholar 

  • Zhao J, Sen-ching Cheung S (2014) Human segmentation by geometrically fusing visible-light and thermal imageries. Multimed Tools Appl 73:61–89

    Article  Google Scholar 

  • Zheng W, Fuller N, Theriault D, Beltke M (2014) A thermal infrared video benchmark for visual analysis. In: IEEE conference on computer vision and pattern recognition workshops

  • Zheng Y, Meng Y (2008) Swarming Particles with multi-feature model for free-selected object tracking. In: IEEE international conference on Intelligent robots and systems, France

  • Zhou H, Fei M, Sadka A, Zhang Y, Li X (2014) Adaptive fusion of particle filtering and spatio-temporal motion energy for human tracking. Pattern Recognit 47:3552–3567

    Article  Google Scholar 

  • Zoidi O, Nikolaidis N, Tefas A, Pitas I (2014) Stereo object tracking with fusion of texture, color and disparity information. Signal Process Image Commun 29:573–589

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Acknowledgments

The authors are grateful Defence Research and Development Organization and Delhi Technological University for financial support to this work. We would also like to thank anominous reviewers for their valuable suggestions.

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Walia, G.S., Kapoor, R. Recent advances on multicue object tracking: a survey. Artif Intell Rev 46, 1–39 (2016). https://doi.org/10.1007/s10462-015-9454-6

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