Results 21 to 30 of about 49,932 (248)

Visual Object Tracking: The Initialisation Problem [PDF]

2018 15th Conference on Computer and Robot Vision (CRV), 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. This BB may contain a large number of background pixels in addition to the object and can lead to parts-based tracking algorithms initialising ...
George De Ath, Richard M. Everson
openaire   +3 more sources

Tracking-by-Segmentation Using Superpixel-Wise Neural Network

IEEE Access, 2018
A tracking-by-segmentation algorithm, which tracks and segments a target object in a video sequence, is proposed in this paper. In the first frame, we segment out the target object in a user-annotated bounding box.
Se-Ho Lee, Won-Dong Jang, Chang-Su Kim
doaj   +1 more source

Global Context Attention for Robust Visual Tracking

Sensors, 2023
Although there have been recent advances in Siamese-network-based visual tracking methods where they show high performance metrics on numerous large-scale visual tracking benchmarks, persistent challenges regarding the distractor objects with similar ...
Janghoon Choi
doaj   +1 more source

Hard Occlusions in Visual Object Tracking [PDF]

, 2020
Accepted at ECCV 2020 Workshop RLQ ...
Thijs P. Kuipers, Devanshu Arya, Deepak K. Gupta   +2 more
openaire   +4 more sources

The Visual Object Tracking VOT2015 Challenge Results [PDF]

2015 IEEE International Conference on Computer Vision Workshop (ICCVW), 2015
The Visual Object Tracking challenge 2015, VOT2015, aims at comparing short-term single-object visual trackers that do not apply pre-learned models of object appearance. Results of 62 trackers are presented. The number of tested trackers makes VOT 2015 the largest benchmark on short-term tracking to date.
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   +11 more sources

Comparative Object Similarity Learning-Based Robust Visual Tracking

IEEE Access, 2019
Tracking-by-detection for visual object tracking is the most popular and successful framework at present. It treats the tracking problem as a classification task and learns information about the target from each tracking result online.
Weiming Yang, Yuliang Liu, Quan Zhang, Yelong Zheng   +3 more
doaj   +1 more source

Conditional random field tracking model based on a visual long short term memory network

Journal of Electronic Science and Technology, 2020
In dense pedestrian tracking, frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories.
Pei-Xin Liu, Zhao-Sheng Zhu, Xiao-Feng Ye, Xiao-Feng Li   +3 more
doaj   +1 more source

Confidence-Guided Frame Skipping to Enhance Object Tracking Speed

Sensors
Object tracking is a challenging task in computer vision. While simple tracking methods offer fast speeds, they often fail to track targets. To address this issue, traditional methods typically rely on complex algorithms.
Yun Gu Lee
doaj   +1 more source

Visual tracking with multilevel feature, similarity attention, color constraint, and global redetection

International Journal of Advanced Robotic Systems, 2021
Visual tracking is fundamental in computer vision tasks. The Siamese-based trackers have shown surprising effectiveness in recent years. However, two points have been neglected: firstly, few of them focus on fusing the image level and semantic level ...
Song Guiling, Zhang Jingyi, Xue Feng, Lu Ru, Yang Qin, Ming Anlong   +5 more
doaj   +1 more source

Spatio-temporal interactive fusion based visual object tracking method

Frontiers in Physics, 2023
Visual object tracking tasks often struggle with utilizing inter-frame correlation information and handling challenges like local occlusion, deformations, and background interference.
Dandan Huang, Siyu Yu, Jin Duan, Yingzhi Wang, Anni Yao, Yiwen Wang, Junhan Xi   +6 more
doaj   +1 more source