Results 11 to 20 of about 346,319 (275)

Visual object tracking performance measures revisited [PDF]

IEEE Transactions on Image Processing, 2016
The problem of visual tracking evaluation is sporting a large variety of performance measures, and largely suffers from lack of consensus about which measures should be used in experiments.
Kristan, Matej, Leonardis, Aleš, Čehovin, Luka   +2 more
core   +5 more sources

Visual Object Tracking in First Person Vision. [PDF]

Int J Comput Vis, 2023
AbstractThe understanding of human-object interactions is fundamental in First Person Vision (FPV). Visual tracking algorithms which follow the objects manipulated by the camera wearer can provide useful information to effectively model such interactions.
Dunnhofer M, Furnari A, Farinella GM, Micheloni C.   +3 more
europepmc   +5 more sources

Two Stages for Visual Object Tracking [PDF]

2021 International Conference on Intelligent Computing, Automation and Applications (ICAA), 2021
Siamese-based trackers have achived promising performance on visual object tracking tasks. Most existing Siamese-based trackers contain two separate branches for tracking, including classification branch and bounding box regression branch. In addition, image segmentation provides an alternative way to obetain the more accurate target region.
Chen, Fei, Zhang, Fuhan, Wang, Xiaodong
openaire   +2 more sources

The Visual Object Tracking VOT2013 Challenge Results [PDF]

2013 IEEE International Conference on Computer Vision Workshops, 2013
Visual tracking has attracted a significant attention in the last few decades. The recent surge in the number of publications on tracking-related problems have made it almost impossible to follow the developments in the field. One of the reasons is that there is a lack of commonly accepted annotated data-sets and standardized evaluation protocols that ...
Kristan, M, Pflugfelder, R, Leonardis, A, Matas, J, Porikli, F, Cehovin, L, Nebehay, G, Fernandez, G, Vojir, T, Gatt, A, Khajenezhad, A, Salahledin, A, Soltani-Farani, A, Zarezade, A, Petrosino, A, Milton, A, Bozorgtabar, B, Li, B, Chan, CS, Heng, C, Ward, D, Kearney, D, Monekosso, D, Karaimer, HC, Rabiee, HR, Zhu, J, Gao, J, Xiao, J, Zhang, J, Xing, J, Huang, K, Lebeda, K, Cao, L, Maresca, ME, Lim, MK, ELHelw, M, Felsberg, M, Remagnino, P, Bowden, R, Goecke, R, Stolkin, R, Lim, SY, Maher, S, Poullot, S, Wong, S, Satoh, S, Chen, W, Hu, W, Zhang, X, Li, Y, Niu, Z   +50 more
openaire   +12 more sources

The Visual Object Tracking VOT2015 Challenge Results [PDF]

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work
Kristan, Matej, Matas, Jiri, Leonardis, Aleš, Felsberg, Michael, Cehovin, Luka, Fernández, Gustavo, Vojir, Tomas, Häger, Gustav, Nebehay, Georg, Pflugfelder, Roman, Gupta, Abhinav, Bibi, Adel, Lukežič, Alan, Garcia-Martin, Alvaro, Saffari, Amir, Petrosino, Alfredo, Montero, Andrés, Varfolomieiev, Anton, Baskurt, Atilla, Zhao, Baojun, Ghanem, Bernard, Martinez, Brais, Lee, Byeongju, Han, Bohyung, Wang, Chaohui, Garcia, Christophe, Zhang, Chunyuan, Schmid, Cordelia, Tao, Dacheng, Kim, Daijin, Huang, Dafei, Prokhorov, Danil, Du, Dawei, Yeung, Dit-Yan, Ribeiro, Eraldo, Shahbaz Khan, Fahad, Porikli, Fatih, Bunyak, Filiz, Zhu, Gao, Seetharaman, Guna, Kieritz, Hilke, Yau, Hing, Li, Hongdong, Qi, Honggang, Bischof, Horst, Possegger, Horst, Lee, Hyemin, Nam, Hyeonseob, Bogun, Ivan, Jeong, Jae-Chan, Cho, Jae-Il, Lee, Jae-Yeong, Zhu, Jianke, Shi, Jianping, Li, Jiatong, Jia, Jiaya, Feng, Jiayi, Gao, Jin, Choi, Jin Young, Kim, Ji-Wan, Lang, Jochen, Martinez, Jose M., Choi, Jongwon, Xing, Junliang, Xue, Kai, Palaniappan, Kannappan, Lebeda, Karel, Alahari, Karteek, Gao, Ke, Yun, Kimin, Wong, Kin Hong, Luo, Lei, Ma, Liang, Ke, Lipeng, Wen, Longyin, Bertinetto, Luca, Pootschi, Mahdieh, Maresca, Mario, Danelljan, Martin, Wen, Mei, Zhang, Mengdan, Arens, Michael, Valstar, Michel, Tang, Ming, Chang, Ming-Ching, Khan, Muhammad, Fan, Nana, Wang, Naiyan, Miksik, Ondrej, Torr, Philip H. S., Wang, Qiang, Martin-Nieto, Rafael, Pelapur, Rengarajan, Bowden, Richard, Laganière, Robert, Moujtahid, Salma, Hare, Sam, Hadfield, Simon, Lyu, Siwei, Li, Siyi, Zhu, Song-Chun, Becker, Stefan, Duffner, Stefan, Hicks, Stephen, Golodetz, Stuart, Choi, Sunglok, Wu, Tianfu, Mauthner, Thomas, Pridmore, Tony, Hu, Weiming, Hübner, Wolfgang, Wang, Xiaomeng, Li, Xin, Shi, Xinchu, Zhao, Xu, Mei, Xue, Shizeng, Yao, Hua, Yang, Li, Yang, Lu, Yang, Li, Yuezun, Chen, Zhaoyun, Huang, Zehua, Chen, Zhe, Zhang, Zhe, He, Zhenyu, Hong, Zhibin   +126 more
openaire   +13 more sources

Semantics-Aware Visual Object Tracking [PDF]

IEEE Transactions on Circuits and Systems for Video Technology, 2019
In this paper, we propose a semantics-aware visual object tracking method, which introduces semantics into the tracking procedure and extends the model of an object with explicit semantics prior to enhancing the robustness of three key aspects of the tracking framework, i.e., appearance model, search scheme, and scale adaptation.
Rui Yao, Guosheng Lin, Chunhua Shen, Yanning Zhang, Qinfeng Shi   +4 more
openaire   +1 more source

Jointly Feature Learning and Selection for Robust Tracking via a Gating Mechanism. [PDF]

PLoS ONE, 2016
To achieve effective visual tracking, a robust feature representation composed of two separate components (i.e., feature learning and selection) for an object is one of the key issues.
Bineng Zhong, Jun Zhang, Pengfei Wang, Jixiang Du, Duansheng Chen   +4 more
doaj   +1 more source

One-Shot Scale and Angle Estimation for Fast Visual Object Tracking

IEEE Access, 2019
Visual object tracking is the process of tracking an arbitrary object in a video, where the bounding box of the object is given in the first frame. Siamese network-based visual object tracking approaches have recently received considerable attention for ...
Dong-Hyun Lee
doaj   +1 more source

Visual Object Tracking: The Initialisation Problem [PDF]

, 2018
Model initialisation is an important component of object tracking. Tracking algorithms are generally provided with the first frame of a sequence and a bounding box (BB) indicating the location of the object.
De Ath, George, Everson, Richard
core   +2 more sources

STResNet_CF Tracker: The Deep Spatiotemporal Features Learning for Correlation Filter Based Robust Visual Object Tracking

IEEE Access, 2019
Constructing a robust appearance model of the visual object is a crucial task for visual object tracking. Recently, more and more studies combine spatial feature with a temporal feature to improve the tracking performance.
Zhengyu Zhu, Bing Liu, Yunbo Rao, Qiao Liu, Rui Zhang   +4 more
doaj   +1 more source