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1. The First Visual Object Tracking Segmentation VOTS2023 Challenge Results

Author

Kristan, Matej, primary, Matas, Jiří, additional, Danelljan, Martin, additional, Felsberg, Michael, additional, Chang, Hyung Jin, additional, Čehovin Zajc, Luka, additional, Lukežič, Alan, additional, Drbohlav, Ondrej, additional, Zhang, Zhongqun, additional, Tran, Khanh-Tung, additional, Vu, Xuan-Son, additional, Björklund, Johanna, additional, Mayer, Christoph, additional, Zhang, Yushan, additional, Ke, Lei, additional, Zhao, Jie, additional, Fernández, Gustavo, additional, Al-Shakarji, Noor, additional, An, Dong, additional, Arens, Michael, additional, Becker, Stefan, additional, Bhat, Goutam, additional, Bullinger, Sebastian, additional, Chan, Antoni B., additional, Chang, Shijie, additional, Chen, Hanyuan, additional, Chen, Xin, additional, Chen, Yan, additional, Chen, Zhenyu, additional, Cheng, Yangming, additional, Cui, Yutao, additional, Deng, Chunyuan, additional, Dong, Jiahua, additional, Dunnhofer, Matteo, additional, Feng, Wei, additional, Fu, Jianlong, additional, Gao, Jie, additional, Han, Ruize, additional, Hao, Zeqi, additional, He, Jun-Yan, additional, He, Keji, additional, He, Zhenyu, additional, Hu, Xiantao, additional, Huang, Kaer, additional, Huang, Yuqing, additional, Jiang, Yi, additional, Kang, Ben, additional, Lan, Jin-Peng, additional, Lee, Hyungjun, additional, Li, Chenyang, additional, Li, Jiahao, additional, Li, Ning, additional, Li, Wangkai, additional, Li, Xiaodi, additional, Li, Xin, additional, Liu, Pengyu, additional, Liu, Yue, additional, Lu, Huchuan, additional, Luo, Bin, additional, Luo, Ping, additional, Ma, Yinchao, additional, Miao, Deshui, additional, Micheloni, Christian, additional, Palaniappan, Kannappan, additional, Park, Hancheol, additional, Paul, Matthieu, additional, Peng, HouWen, additional, Qian, Zekun, additional, Rahmon, Gani, additional, Scherer-Negenborn, Norbert, additional, Shao, Pengcheng, additional, Shin, Wooksu, additional, Kazemi, Elham Soltani, additional, Song, Tianhui, additional, Stiefelhagen, Rainer, additional, Sun, Rui, additional, Tang, Chuanming, additional, Tang, Zhangyong, additional, Toubal, Imad Eddine, additional, Valmadre, Jack, additional, van de Weijer, Joost, additional, Van Gool, Luc, additional, Vira, Jash, additional, Vujasinović, Stèphane, additional, Wan, Cheng, additional, Wan, Jia, additional, Wang, Dong, additional, Wang, Fei, additional, Wang, Feifan, additional, Wang, He, additional, Wang, Limin, additional, Wang, Song, additional, Wang, Yaowei, additional, Wang, Zhepeng, additional, Wu, Gangshan, additional, Wu, Jiannan, additional, Wu, Qiangqiang, additional, Wu, Xiaojun, additional, Xiao, Anqi, additional, Xie, Jinxia, additional, Xu, Chenlong, additional, Xu, Min, additional, Xu, Tianyang, additional, Xu, Yuanyou, additional, Yan, Bin, additional, Yang, Dawei, additional, Yang, Ming-Hsuan, additional, Yang, Tianyu, additional, Yang, Yi, additional, Yang, Zongxin, additional, Yin, Xuanwu, additional, Yu, Fisher, additional, Yu, Hongyuan, additional, Yu, Qianjin, additional, Yu, Weichen, additional, Yuan, YongSheng, additional, Yuan, Zehuan, additional, Zhang, Jianlin, additional, Zhang, Lu, additional, Zhang, Tianzhu, additional, Zhao, Guodongfang, additional, Zhao, Shaochuan, additional, Zheng, Yaozong, additional, Zhong, Bineng, additional, Zhu, Jiawen, additional, Zhu, Xuefeng, additional, Zhuang, Yueting, additional, Zong, ChengAo, additional, and Zuo, Kunlong, additional

Published
2023
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5. Multiple Human Association and Tracking From Egocentric and Complementary Top Views.

Author

Han, Ruize, Feng, Wei, Zhang, Yujun, Zhao, Jiewen, and Wang, Song

Subjects
*WEARABLE cameras, *INTEGER programming, *OVERTIME, *VIDEO surveillance, *TRACKING radar, *ARTIFICIAL satellite tracking, *CAMERAS
Abstract

Crowded scene surveillance can significantly benefit from combining egocentric-view and its complementary top-view cameras. A typical setting is an egocentric-view camera, e.g., a wearable camera on the ground capturing rich local details, and a top-view camera, e.g., a drone-mounted one from high altitude providing a global picture of the scene. To collaboratively analyze such complementary-view videos, an important task is to associate and track multiple people across views and over time, which is challenging and differs from classical human tracking, since we need to not only track multiple subjects in each video, but also identify the same subjects across the two complementary views. This paper formulates it as a constrained mixed integer programming problem, wherein a major challenge is how to effectively measure subjects similarity over time in each video and across two views. Although appearance and motion consistencies well apply to over-time association, they are not good at connecting two highly different complementary views. To this end, we present a spatial distribution based approach to reliable cross-view subject association. We also build a dataset to benchmark this new challenging task. Extensive experiments verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Published
2022
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6. Multi-View Multi-Human Association With Deep Assignment Network.

Author

Han, Ruize, Wang, Yun, Yan, Haomin, Feng, Wei, and Wang, Song

Subjects
*VIDEO surveillance, *WEARABLE cameras, *FEATURE extraction
Abstract

Identifying the same persons across different views plays an important role in many vision applications. In this paper, we study this important problem, denoted as Multi-view Multi-Human Association (MvMHA), on multi-view images that are taken by different cameras at the same time. Different from previous works on human association across two views, this paper is focused on more general and challenging scenarios of more than two views, and none of these views are fixed or priorly known. In addition, each involved person may be present in all the views or only a subset of views, which are also not priorly known. We develop a new end-to-end deep-network based framework to address this problem. First, we use an appearance-based deep network to extract the feature of each detected subject on each image. We then compute pairwise-similarity scores between all the detected subjects and construct a comprehensive affinity matrix. Finally, we propose a Deep Assignment Network (DAN) to transform the affinity matrix into an assignment matrix, which provides a binary assignment result for MvMHA. We build both a synthetic dataset and a real image dataset to verify the effectiveness of the proposed method. We also test the trained network on other three public datasets, resulting in very good cross-domain performance. [ABSTRACT FROM AUTHOR]

Published
2022
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8. The Seventh Visual Object Tracking VOT2019 Challenge Results

Author

Kristan, Matej, primary, Matas, Jiri, additional, Leonardis, Ales, additional, Felsberg, Michael, additional, Pflugfelder, Roman, additional, Kamarainen, Joni-Kristian, additional, Cehovin Zajc, Luka, additional, Drbohlav, Ondrej, additional, Lukezic, Alan, additional, Berg, Amanda, additional, Eldesokey, Abdelrahman, additional, Kapyla, Jani, additional, Fernandez, Gustavo, additional, Gonzalez-Garcia, Abel, additional, Memarmoghadam, Alireza, additional, Lu, Andong, additional, He, Anfeng, additional, Varfolomieiev, Anton, additional, Chan, Antoni, additional, Tripathi, Ardhendu Shekhar, additional, Smeulders, Arnold, additional, Pedasingu, Bala Suraj, additional, Chen, Bao Xin, additional, Zhang, Baopeng, additional, Wu, Baoyuan, additional, Li, Bi, additional, He, Bin, additional, Yan, Bin, additional, Bai, Bing, additional, Li, Bing, additional, Li, Bo, additional, Kim, Byeong Hak, additional, Ma, Chao, additional, Fang, Chen, additional, Qian, Chen, additional, Chen, Cheng, additional, Li, Chenglong, additional, Zhang, Chengquan, additional, Tsai, Chi-Yi, additional, Luo, Chong, additional, Micheloni, Christian, additional, Zhang, Chunhui, additional, Tao, Dacheng, additional, Gupta, Deepak, additional, Song, Dejia, additional, Wang, Dong, additional, Gavves, Efstratios, additional, Yi, Eunu, additional, Khan, Fahad Shahbaz, additional, Zhang, Fangyi, additional, Wang, Fei, additional, Zhao, Fei, additional, Ath, George De, additional, Bhat, Goutam, additional, Chen, Guangqi, additional, Wang, Guangting, additional, Li, Guoxuan, additional, Cevikalp, Hakan, additional, Du, Hao, additional, Zhao, Haojie, additional, Saribas, Hasan, additional, Jung, Ho Min, additional, Bai, Hongliang, additional, Yu, Hongyuan, additional, Peng, Houwen, additional, Lu, Huchuan, additional, Li, Hui, additional, Li, Jiakun, additional, Li, Jianhua, additional, Fu, Jianlong, additional, Chen, Jie, additional, Gao, Jie, additional, Zhao, Jie, additional, Tang, Jin, additional, Li, Jing, additional, Wu, Jingjing, additional, Liu, Jingtuo, additional, Wang, Jinqiao, additional, Qi, Jinqing, additional, Zhang, Jinyue, additional, Tsotsos, John K., additional, Lee, Jong Hyuk, additional, Weijer, Joost van de, additional, Kittler, Josef, additional, Lee, Jun Ha, additional, Zhuang, Junfei, additional, Zhang, Kangkai, additional, Wang, Kangkang, additional, Dai, Kenan, additional, Chen, Lei, additional, Liu, Lei, additional, Guo, Leida, additional, Zhang, Li, additional, Wang, Liang, additional, Wang, Liangliang, additional, Zhang, Lichao, additional, Wang, Lijun, additional, Zhou, Lijun, additional, Zheng, Linyu, additional, Rout, Litu, additional, Van Gool, Luc, additional, Bertinetto, Luca, additional, Danelljan, Martin, additional, Dunnhofer, Matteo, additional, Ni, Meng, additional, Kim, Min Young, additional, Tang, Ming, additional, Yang, Ming-Hsuan, additional, Paluru, Naveen, additional, Martinel, Niki, additional, Xu, Pengfei, additional, Zhang, Pengfei, additional, Zheng, Pengkun, additional, Zhang, Pengyu, additional, Torr, Philip H.S., additional, Wang, Qi Zhang Qiang, additional, Guo, Qing, additional, Timofte, Radu, additional, Gorthi, Rama Krishna, additional, Everson, Richard, additional, Han, Ruize, additional, Zhang, Ruohan, additional, You, Shan, additional, Zhao, Shao-Chuan, additional, Zhao, Shengwei, additional, Li, Shihu, additional, Li, Shikun, additional, Ge, Shiming, additional, Bai, Shuai, additional, Guan, Shuosen, additional, Xing, Tengfei, additional, Xu, Tianyang, additional, Yang, Tianyu, additional, Zhang, Ting, additional, Vojir, Tomas, additional, Feng, Wei, additional, Hu, Weiming, additional, Wang, Weizhao, additional, Tang, Wenjie, additional, Zeng, Wenjun, additional, Liu, Wenyu, additional, Chen, Xi, additional, Qiu, Xi, additional, Bai, Xiang, additional, Wu, Xiao-Jun, additional, Yang, Xiaoyun, additional, Chen, Xier, additional, Li, Xin, additional, Sun, Xing, additional, Chen, Xingyu, additional, Tian, Xinmei, additional, Tang, Xu, additional, Zhu, Xue-Feng, additional, Huang, Yan, additional, Chen, Yanan, additional, Lian, Yanchao, additional, Gu, Yang, additional, Liu, Yang, additional, Chen, Yanjie, additional, Zhang, Yi, additional, Xu, Yinda, additional, Wang, Yingming, additional, Li, Yingping, additional, Zhou, Yu, additional, Dong, Yuan, additional, Xu, Yufei, additional, Zhang, Yunhua, additional, Li, Yunkun, additional, Luo, Zeyu Wang Zhao, additional, Zhang, Zhaoliang, additional, Feng, Zhen-Hua, additional, He, Zhenyu, additional, Song, Zhichao, additional, Chen, Zhihao, additional, Zhang, Zhipeng, additional, Wu, Zhirong, additional, Xiong, Zhiwei, additional, Huang, Zhongjian, additional, Teng, Zhu, additional, and Ni, Zihan, additional

Published
2019
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9. Fast Learning of Spatially Regularized and Content Aware Correlation Filter for Visual Tracking.

Author

Han, Ruize, Feng, Wei, and Wang, Song

Subjects
*TIME complexity, *ENERGY function, *ONLINE education, *ITERATIVE learning control
Abstract

With a good balance between accuracy and speed, correlation filter (CF) has become a popular and dominant visual object tracking scheme. It implicitly extends the training samples by circular shifts of a given target patch, which serve as negative samples for fast online learning of the filters. Since all these shifted patches are not real negative samples of the target, CF tracking scheme suffers from the annoying boundary effects that can greatly harm the tracking performance, especially under challenging situations, like occlusion and fast temporal variation. Spatial regularization is known as a potent way to alleviate such boundary effects, but with the cost of highly increased time complexity, caused by complex optimization imported by spatial regularization. In this paper, we propose a new fast learning approach to content-aware spatial regularization, namely weighted sample based CF tracking (WSCF). In WSCF, specifically, we present a simple yet effective energy function that implicitly weighs different training samples by spatial deviations. With the energy function, the learning of correlation filters is composed of two subproblems with closed-form solution and can be efficiently solved in an alternate way. We further develop a content-aware updating strategy to dynamically refine the weight distribution to well adapt to the temporal variations of the target and background. Finally, the proposed WSCF is used to enhance two state-of-the-art CF trackers to significantly boost their tracking accuracy, with little sacrifice on the tracking speed. Extensive experiments on five benchmarks validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Published
2020
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10. Selective Spatial Regularization by Reinforcement Learned Decision Making for Object Tracking.

Author

Guo, Qing, Han, Ruize, Feng, Wei, Chen, Zhihao, and Wan, Liang

Subjects
*OBJECT tracking (Computer vision), *REINFORCEMENT learning, *MARKOV processes, *DECISION making, *LEARNING strategies
Abstract

Spatial regularization (SR) is known as an effective tool to alleviate the boundary effect of correlation filter (CF), a successful visual object tracking scheme, from which a number of state-of-the-art visual object trackers can be stemmed. Nevertheless, SR highly increases the optimization complexity of CF and its target-driven nature makes spatially-regularized CF trackers may easily lose the occluded targets or the targets surrounded by other similar objects. In this paper, we propose selective spatial regularization (SSR) for CF-tracking scheme. It can achieve not only higher accuracy and robustness, but also higher speed compared with spatially-regularized CF trackers. Specifically, rather than simply relying on foreground information, we extend the objective function of CF tracking scheme to learn the target-context-regularized filters using target-context-driven weight maps. We then formulate the online selection of these weight maps as a decision making problem by a Markov Decision Process (MDP), where the learning of weight map selection is equivalent to policy learning of the MDP that is solved by a reinforcement learning strategy. Moreover, by adding a special state, representing not-updating filters, in the MDP, we can learn when to skip unnecessary or erroneous filter updating, thus accelerating the online tracking. Finally, the proposed SSR is used to equip three popular spatially-regularized CF trackers to significantly boost their tracking accuracy, while achieving much faster online tracking speed. Besides, extensive experiments on five benchmarks validate the effectiveness of SSR. [ABSTRACT FROM AUTHOR]

Published
2020
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12. Dynamic Saliency-Aware Regularization for Correlation Filter-Based Object Tracking.

Author

Feng, Wei, Han, Ruize, Guo, Qing, Zhu, Jianke, and Wang, Song

Subjects
*OBJECT tracking (Computer vision), *MATHEMATICAL regularization, *ENERGY function, *FEATURE extraction
Abstract

With a good balance between tracking accuracy and speed, correlation filter (CF) has become one of the best object tracking frameworks, based on which many successful trackers have been developed. Recently, spatially regularized CF tracking (SRDCF) has been developed to remedy the annoying boundary effects of CF tracking, thus further boosting the tracking performance. However, SRDCF uses a fixed spatial regularization map constructed from a loose bounding box and its performance inevitably degrades when the target or background show significant variations, such as object deformation or occlusion. To address this problem, we propose a new dynamic saliency-aware regularized CF tracking (DSAR-CF) scheme. In DSAR-CF, a simple yet effective energy function, which reflects the object saliency and tracking reliability in the spatial–temporal domain, is defined to guide the online updating of the regularization weight map using an efficient level-set algorithm. Extensive experiments validate that the proposed DSAR-CF leads to better performance in terms of accuracy and speed than the original SRDCF. [ABSTRACT FROM AUTHOR]

Published
2019
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