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Journal articles on the topic 'Visual tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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1

ZANG, Chuantao, Yoshihide ENDO, and Koichi HASHIMOTO. "2P1-D20 GPU accelerating visual tracking." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2010 (2010): _2P1—D20_1—_2P1—D20_4. http://dx.doi.org/10.1299/jsmermd.2010._2p1-d20_1.

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2

Roberts, J., and D. Charnley. "Parallel Visual Tracking." IFAC Proceedings Volumes 26, no. 1 (April 1993): 127–32. http://dx.doi.org/10.1016/s1474-6670(17)49287-1.

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3

Yuan, Heng, Wen-Tao Jiang, Wan-Jun Liu, and Sheng-Chong Zhang. "Visual node prediction for visual tracking." Multimedia Systems 25, no. 3 (January 30, 2019): 263–72. http://dx.doi.org/10.1007/s00530-019-00603-1.

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4

Lou, Jianguang, Tieniu Tan, and Weiming Hu. "Visual vehicle tracking algorithm." Electronics Letters 38, no. 18 (2002): 1024. http://dx.doi.org/10.1049/el:20020692.

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5

Ming Yang, Ying Wu, and Gang Hua. "Context-Aware Visual Tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 7 (July 2009): 1195–209. http://dx.doi.org/10.1109/tpami.2008.146.

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6

Zhang, Lei, Yanjie Wang, Honghai Sun, Zhijun Yao, and Shuwen He. "Robust Visual Correlation Tracking." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/238971.

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Recent years have seen greater interests in the tracking-by-detection methods in the visual object tracking, because of their excellent tracking performance. But most existing methods fix the scale which makes the trackers unreliable to handle large scale variations in complex scenes. In this paper, we decompose the tracking into target translation and scale prediction. We adopt a scale estimation approach based on the tracking-by-detection framework, develop a new model update scheme, and present a robust correlation tracking algorithm with discriminative correlation filters. The approach works by learning the translation and scale correlation filters. We obtain the target translation and scale by finding the maximum output response of the learned correlation filters and then online update the target models. Extensive experiments results on 12 challenging benchmark sequences show that the proposed tracking approach reduces the average center location error (CLE) by 6.8 pixels, significantly improves the performance by 17.5% in the average success rate (SR) and by 5.4% in the average distance precision (DP) compared to the second best one of the other five excellent existing tracking algorithms, and is robust to appearance variations introduced by scale variations, pose variations, illumination changes, partial occlusion, fast motion, rotation, and background clutter.

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7

Roberts, J. M., and D. Charnley. "Parallel attentive visual tracking." Engineering Applications of Artificial Intelligence 7, no. 2 (April 1994): 205–15. http://dx.doi.org/10.1016/0952-1976(94)90024-8.

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8

Wang, Hesheng, Yun-Hui Liu, and Weidong Chen. "Uncalibrated Visual Tracking Control Without Visual Velocity." IEEE Transactions on Control Systems Technology 18, no. 6 (November 2010): 1359–70. http://dx.doi.org/10.1109/tcst.2010.2041457.

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9

Shi, Liangtao, Bineng Zhong, Qihua Liang, Ning Li, Shengping Zhang, and Xianxian Li. "Explicit Visual Prompts for Visual Object Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (March 24, 2024): 4838–46. http://dx.doi.org/10.1609/aaai.v38i5.28286.

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How to effectively exploit spatio-temporal information is crucial to capture target appearance changes in visual tracking. However, most deep learning-based trackers mainly focus on designing a complicated appearance model or template updating strategy, while lacking the exploitation of context between consecutive frames and thus entailing the when-and-how-to-update dilemma. To address these issues, we propose a novel explicit visual prompts framework for visual tracking, dubbed EVPTrack. Specifically, we utilize spatio-temporal tokens to propagate information between consecutive frames without focusing on updating templates. As a result, we cannot only alleviate the challenge of when-to-update, but also avoid the hyper-parameters associated with updating strategies. Then, we utilize the spatio-temporal tokens to generate explicit visual prompts that facilitate inference in the current frame. The prompts are fed into a transformer encoder together with the image tokens without additional processing. Consequently, the efficiency of our model is improved by avoiding how-to-update. In addition, we consider multi-scale information as explicit visual prompts, providing multiscale template features to enhance the EVPTrack's ability to handle target scale changes. Extensive experimental results on six benchmarks (i.e., LaSOT, LaSOText, GOT-10k, UAV123, TrackingNet, and TNL2K.) validate that our EVPTrack can achieve competitive performance at a real-time speed by effectively exploiting both spatio-temporal and multi-scale information. Code and models are available at https://github.com/GXNU-ZhongLab/EVPTrack.

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10

Zhang, Yue, Huibin Lu, and Xingwang Du. "ROAM-based visual tracking method." Journal of Physics: Conference Series 1732 (January 2021): 012064. http://dx.doi.org/10.1088/1742-6596/1732/1/012064.

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11

BĂNICĂ, Marian Valentin, Anamaria RĂDOI, and Petrișor Valentin PÂRVU. "ONBOARD VISUAL TRACKING FOR UAV’S." Scientific Journal of Silesian University of Technology. Series Transport 105 (December 1, 2019): 35–48. http://dx.doi.org/10.20858/sjsutst.2019.105.4.

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12

ZHANG, Tiansa, Chunlei HUO, Zhiqiang ZHOU, and Bo WANG. "Faster-ADNet for Visual Tracking." IEICE Transactions on Information and Systems E102.D, no. 3 (March 1, 2019): 684–87. http://dx.doi.org/10.1587/transinf.2018edl8214.

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13

Wedel, Michel, and Rik Pieters. "Eye Tracking for Visual Marketing." Foundations and Trends® in Marketing 1, no. 4 (2006): 231–320. http://dx.doi.org/10.1561/1700000011.

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14

Chunhua Shen, Junae Kim, and Hanzi Wang. "Generalized Kernel-Based Visual Tracking." IEEE Transactions on Circuits and Systems for Video Technology 20, no. 1 (January 2010): 119–30. http://dx.doi.org/10.1109/tcsvt.2009.2031393.

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15

Munich, M. E., and P. Perona. "Visual identification by signature tracking." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 2 (February 2003): 200–217. http://dx.doi.org/10.1109/tpami.2003.1177152.

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16

Ma, Bo, Lianghua Huang, Jianbing Shen, Ling Shao, Ming-Hsuan Yang, and Fatih Porikli. "Visual Tracking Under Motion Blur." IEEE Transactions on Image Processing 25, no. 12 (December 2016): 5867–76. http://dx.doi.org/10.1109/tip.2016.2615812.

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17

Chen, Kai, and Wenbing Tao. "Convolutional Regression for Visual Tracking." IEEE Transactions on Image Processing 27, no. 7 (July 2018): 3611–20. http://dx.doi.org/10.1109/tip.2018.2819362.

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18

Pei, Zhijun, and Lei Han. "Visual Tracking Using L2 Minimization." MATEC Web of Conferences 61 (2016): 02020. http://dx.doi.org/10.1051/matecconf/20166102020.

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19

Zhou, Jiawei, and Shahram Payandeh. "Visual Tracking of Laparoscopic Instruments." Journal of Automation and Control Engineering 2, no. 3 (2014): 234–41. http://dx.doi.org/10.12720/joace.2.3.234-241.

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20

Yao, Rui, Guosheng Lin, Chunhua Shen, Yanning Zhang, and Qinfeng Shi. "Semantics-Aware Visual Object Tracking." IEEE Transactions on Circuits and Systems for Video Technology 29, no. 6 (June 2019): 1687–700. http://dx.doi.org/10.1109/tcsvt.2018.2848358.

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21

Kim, Minyoung. "Correlation-based incremental visual tracking." Pattern Recognition 45, no. 3 (March 2012): 1050–60. http://dx.doi.org/10.1016/j.patcog.2011.08.026.

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22

Li, Zhidong, Weihong Wang, Yang Wang, Fang Chen, and Yi Wang. "Visual tracking by proto-objects." Pattern Recognition 46, no. 8 (August 2013): 2187–201. http://dx.doi.org/10.1016/j.patcog.2013.01.020.

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23

Sergeant, D., R. Boyle, and M. Forbes. "Computer visual tracking of poultry." Computers and Electronics in Agriculture 21, no. 1 (September 1998): 1–18. http://dx.doi.org/10.1016/s0168-1699(98)00025-8.

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24

Bernardino, Alexandre, and José Santos-Victor. "Visual behaviours for binocular tracking." Robotics and Autonomous Systems 25, no. 3-4 (November 1998): 137–46. http://dx.doi.org/10.1016/s0921-8890(98)00043-8.

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25

Tannenbaum, Allen, Anthony Yezzi, and Alex Goldstein. "Visual Tracking and Object Recognition." IFAC Proceedings Volumes 34, no. 6 (July 2001): 1539–42. http://dx.doi.org/10.1016/s1474-6670(17)35408-3.

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26

Shao, Y., J. E. W. Mayhew, and Y. Zheng. "Model-driven active visual tracking." Real-Time Imaging 4, no. 5 (January 1998): 349–59. http://dx.doi.org/10.1016/s1077-2014(98)90004-3.

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27

Yun, Xiao, and Gang Xiao. "Spiral visual and motional tracking." Neurocomputing 249 (August 2017): 117–27. http://dx.doi.org/10.1016/j.neucom.2017.03.070.

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28

Xu, Weicun, Qingjie Zhao, and Dongbing Gu. "Fragmentation handling for visual tracking." Signal, Image and Video Processing 8, no. 8 (November 28, 2012): 1639–49. http://dx.doi.org/10.1007/s11760-012-0406-1.

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29

Mei, Xue, Tianzhu Zhang, Huchuan Lu, Ming-Hsuan Yang, Kyoung Mu Lee, and Horst Bischof. "Special Issue on Visual Tracking." Computer Vision and Image Understanding 153 (December 2016): 1–2. http://dx.doi.org/10.1016/j.cviu.2016.11.001.

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30

Chli, Margarita, and Andrew J. Davison. "Active matching for visual tracking." Robotics and Autonomous Systems 57, no. 12 (December 2009): 1173–87. http://dx.doi.org/10.1016/j.robot.2009.07.010.

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31

Zhou, Yu, Xiang Bai, Wenyu Liu, and Longin Jan Latecki. "Similarity Fusion for Visual Tracking." International Journal of Computer Vision 118, no. 3 (January 25, 2016): 337–63. http://dx.doi.org/10.1007/s11263-015-0879-9.

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32

王, 楠洋. "A Review of Visual Tracking." Computer Science and Application 08, no. 01 (2018): 35–42. http://dx.doi.org/10.12677/csa.2018.81006.

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33

Dai, Bo, Zhiqiang Hou, Wangsheng Yu, Feng Zhu, Xin Wang, and Zefenfen Jin. "Visual tracking via ensemble autoencoder." IET Image Processing 12, no. 7 (July 1, 2018): 1214–21. http://dx.doi.org/10.1049/iet-ipr.2017.0486.

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34

BANDOPADHAY, AMIT, and DANA H. BALLARD. "Egomotion perception using visual tracking." Computational Intelligence 7, no. 1 (February 1991): 39–47. http://dx.doi.org/10.1111/j.1467-8640.1991.tb00333.x.

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35

Yokomichi, Masahiro, and Yuki Nakagama. "Multimodal MSEPF for visual tracking." Artificial Life and Robotics 17, no. 2 (August 28, 2012): 257–62. http://dx.doi.org/10.1007/s10015-012-0050-4.

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36

Quinlan, P. "Visual tracking and feature binding." Ophthalmic and Physiological Optics 14, no. 4 (October 1994): 439. http://dx.doi.org/10.1016/0275-5408(94)90190-2.

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37

Rao, Jinjun, Kai Xu, Jinbo Chen, Jingtao Lei, Zhen Zhang, Qiuyu Zhang, Wojciech Giernacki, and Mei Liu. "Sea-Surface Target Visual Tracking with a Multi-Camera Cooperation Approach." Sensors 22, no. 2 (January 17, 2022): 693. http://dx.doi.org/10.3390/s22020693.

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Cameras are widely used in the detection and tracking of moving targets. Compared to target visual tracking using a single camera, cooperative tracking based on multiple cameras has advantages including wider visual field, higher tracking reliability, higher precision of target positioning and higher possibility of multiple-target visual tracking. With vast ocean and sea surfaces, it is a challenge using multiple cameras to work together to achieve specific target tracking and detection, and it will have a wide range of application prospects. According to the characteristics of sea-surface moving targets and visual images, this study proposed and designed a sea-surface moving-target visual detection and tracking system with a multi-camera cooperation approach. In the system, the technologies of moving target detection, tracking, and matching are studied, and the strategy to coordinate multi-camera cooperation is proposed. The comprehensive experiments of cooperative sea-surface moving-target visual tracking show that the method used in this study has improved performance compared with contrapositive methods, and the proposed system can meet the needs of multi-camera cooperative visual tracking of moving targets on the sea surface.

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38

Banu, Rubeena, and M. H. Sidram. "Window Based Min-Max Feature Extraction for Visual Object Tracking." Indian Journal Of Science And Technology 15, no. 40 (October 27, 2022): 2047–55. http://dx.doi.org/10.17485/ijst/v15i40.1395.

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39

Peng, Chao, Danhua Cao, Yubin Wu, and Qun Yang. "Robot visual guide with Fourier-Mellin based visual tracking." Frontiers of Optoelectronics 12, no. 4 (June 8, 2019): 413–21. http://dx.doi.org/10.1007/s12200-019-0862-0.

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40

Choi, Janghoon. "Global Context Attention for Robust Visual Tracking." Sensors 23, no. 5 (March 1, 2023): 2695. http://dx.doi.org/10.3390/s23052695.

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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 appearances to the target object still remain. To address these aforementioned issues, we propose a novel global context attention module for visual tracking, where the proposed module can extract and summarize the holistic global scene information to modulate the target embedding for improved discriminability and robustness. Our global context attention module receives a global feature correlation map to elicit the contextual information from a given scene and generates the channel and spatial attention weights to modulate the target embedding to focus on the relevant feature channels and spatial parts of the target object. Our proposed tracking algorithm is tested on large-scale visual tracking datasets, where we show improved performance compared to the baseline tracking algorithm while achieving competitive performance with real-time speed. Additional ablation experiments also validate the effectiveness of the proposed module, where our tracking algorithm shows improvements in various challenging attributes of visual tracking.

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41

Vihlman, Mikko, and Arto Visala. "Optical Flow in Deep Visual Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12112–19. http://dx.doi.org/10.1609/aaai.v34i07.6890.

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Single-target tracking of generic objects is a difficult task since a trained tracker is given information present only in the first frame of a video. In recent years, increasingly many trackers have been based on deep neural networks that learn generic features relevant for tracking. This paper argues that deep architectures are often fit to learn implicit representations of optical flow. Optical flow is intuitively useful for tracking, but most deep trackers must learn it implicitly. This paper is among the first to study the role of optical flow in deep visual tracking. The architecture of a typical tracker is modified to reveal the presence of implicit representations of optical flow and to assess the effect of using the flow information more explicitly. The results show that the considered network learns implicitly an effective representation of optical flow. The implicit representation can be replaced by an explicit flow input without a notable effect on performance. Using the implicit and explicit representations at the same time does not improve tracking accuracy. The explicit flow input could allow constructing lighter networks for tracking.

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42

Chen, Yuantao, Weihong Xu, Fangjun Kuang, and Shangbing Gao. "The Research and Application of Visual Saliency and Adaptive Support Vector Machine in Target Tracking Field." Computational and Mathematical Methods in Medicine 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/925341.

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The efficient target tracking algorithm researches have become current research focus of intelligent robots. The main problems of target tracking process in mobile robot face environmental uncertainty. They are very difficult to estimate the target states, illumination change, target shape changes, complex backgrounds, and other factors and all affect the occlusion in tracking robustness. To further improve the target tracking’s accuracy and reliability, we present a novel target tracking algorithm to use visual saliency and adaptive support vector machine (ASVM). Furthermore, the paper’s algorithm has been based on the mixture saliency of image features. These features include color, brightness, and sport feature. The execution process used visual saliency features and those common characteristics have been expressed as the target’s saliency. Numerous experiments demonstrate the effectiveness and timeliness of the proposed target tracking algorithm in video sequences where the target objects undergo large changes in pose, scale, and illumination.

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43

ZhongMing Liao and Azlan Ismail. "Performance of Correlational Filtering and Deep Learning Based Single Target Tracking Algorithms." Journal of Smart Science and Technology 3, no. 1 (March 30, 2023): 63–79. http://dx.doi.org/10.24191/jsst.v3i1.42.

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Visual target tracking is an important research element in the field of computer vision. The applications are very wide. In terms of the computer vision field, deep learning has achieved remarkable results. It has broken through many complex problems that are difficult to be solved by traditional algorithms. Therefore, reviewing the visual target tracking algorithms based on deep learning from different perspectives is important. This paper closely follows the tracking framework of target tracking algorithms and discusses in detail the traditional visual target tracking methods, the mainstream single target tracking algorithms based on correlation filtering, and the video single target tracking algorithms based on deep learning. Experiments were conducted on OTB100 and VOT2018 benchmark datasets, and the experimental data obtained were analysed to derive two visual single-target tracking algorithms with optimal tracking performance. Finally, the future development of tracking algorithms is envisioned.

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44

Battelli, Lorella, George A. Alvarez, Thomas Carlson, and Alvaro Pascual-Leone. "The Role of the Parietal Lobe in Visual Extinction Studied with Transcranial Magnetic Stimulation." Journal of Cognitive Neuroscience 21, no. 10 (October 2009): 1946–55. http://dx.doi.org/10.1162/jocn.2008.21149.

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Interhemispheric competition between homologous areas in the human brain is believed to be involved in a wide variety of human behaviors from motor activity to visual perception and particularly attention. For example, patients with lesions in the posterior parietal cortex are unable to selectively track objects in the contralesional side of visual space when targets are simultaneously present in the ipsilesional visual field, a form of visual extinction. Visual extinction may arise due to an imbalance in the normal interhemispheric competition. To directly assess the issue of reciprocal inhibition, we used fMRI to localize those brain regions active during attention-based visual tracking and then applied low-frequency repetitive transcranial magnetic stimulation over identified areas in the left and right intraparietal sulcus to asses the behavioral effects on visual tracking. We induced a severe impairment in visual tracking that was selective for conditions of simultaneous tracking in both visual fields. Our data show that the parietal lobe is essential for visual tracking and that the two hemispheres compete for attentional resources during tracking. Our results provide a neuronal basis for visual extinction in patients with parietal lobe damage.

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45

Jung, Sangkil, Jinseok Lee, and Sangjin Hong. "Statistical Estimation and Adaptation for Visual Compensation in Object Tracking." International Journal of Distributed Sensor Networks 5, no. 5 (October 1, 2009): 437–62. http://dx.doi.org/10.1080/15501320802581524.

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The multi-modal tracking model in [ 1 ] enables the on-the-fly error compensation with low complexity by adopting acoustic sensors for the main tracking task and visual sensors for correcting possible tracking errors. The visual compensation process in the model is indispensable to the accurate tracking task in a dynamic object movement. This article proposes an algorithm to approximate the successful visual compensation rate appearing in the multi-modal tracking system. The acoustic sampling interval of the object signal and the random occurrence of transmission delays of multi-modal data are critical to the compensation process. Therefore, by using the two key factors as parameters, the algorithm called SEA can estimate the successful visual compensation of a tracking system. After we build up a tracking system, it is required to maintain the system at a certain level of tracking accuracy. This task can be done by controlling the aforementioned parameters since the visual compensation influences the tracking accuracy. Thus, we propose another algorithm called SEA2 for the parameter adaptation. The algorithm controls only acoustic sampling interval due to the easiness of adjustment and having the main impact on the success of the visual compensation. From the algorithm validation, we show the SEA properly quantifies the visual compensation process successfully occurring in the tracking scenarios, and SEA2 is feasible for parameter adaptation and achieving the target level of accuracy.

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46

Zhen, Xinxin, Shumin Fei, Yinmin Wang, and Wei Du. "A Visual Object Tracking Algorithm Based on Improved TLD." Algorithms 13, no. 1 (January 1, 2020): 15. http://dx.doi.org/10.3390/a13010015.

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Visual object tracking is an important research topic in the field of computer vision. Tracking–learning–detection (TLD) decomposes the tracking problem into three modules—tracking, learning, and detection—which provides effective ideas for solving the tracking problem. In order to improve the tracking performance of the TLD tracker, three improvements are proposed in this paper. The built-in tracking module is replaced with a kernelized correlation filter (KCF) algorithm based on the histogram of oriented gradient (HOG) descriptor in the tracking module. Failure detection is added for the response of KCF to identify whether KCF loses the target. A more specific detection area of the detection module is obtained through the estimated location provided by the tracking module. With the above operations, the scanning area of object detection is reduced, and a full frame search is required in the detection module if objects fails to be tracked in the tracking module. Comparative experiments were conducted on the object tracking benchmark (OTB) and the results showed that the tracking speed and accuracy was improved. Further, the TLD tracker performed better in different challenging scenarios with the proposed method, such as motion blur, occlusion, and environmental changes. Moreover, the improved TLD achieved outstanding tracking performance compared with common tracking algorithms.

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47

WANG, DONG, GANG YANG, and HUCHUAN LU. "TRI-TRACKING: COMBINING THREE INDEPENDENT VIEWS FOR ROBUST VISUAL TRACKING." International Journal of Image and Graphics 12, no. 03 (July 2012): 1250021. http://dx.doi.org/10.1142/s0219467812500210.

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Robust tracking is a challenging problem, due to intrinsic appearance variability of objects caused by in-plane or out-plane rotation and extrinsic factors change such as illumination, occlusion, background clutter and local blur. In this paper, we present a novel tri-tracking framework combining different views (different models using independent features) for robust object tracking. This new tracking framework exploits a hybrid discriminative generative model based on online semi-supervised learning. We only need the first frame for parameters initialization, and then the tracking process is automatic in the remaining frames, with updating the model online to capture the changes of both object appearance and background. There are three main contributions in our tri-tracking approach. First, we propose a tracking framework for combining generative model and discriminative model, together with different cues that complement each other. Second, by introducing a third tracker, we provide a solution to the problem that it is difficult to combine two classification results in co-training framework when they are opposite. Third, we propose a principle way for combing different views, which based on their Discriminative power. We conduct experiments on some challenging videos, the results from which demonstrate that the proposed tri-tracking framework is robust.

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48

Li, Kai, Lihua Cai, Guangjian He, and Xun Gong. "MATI: Multimodal Adaptive Tracking Integrator for Robust Visual Object Tracking." Sensors 24, no. 15 (July 29, 2024): 4911. http://dx.doi.org/10.3390/s24154911.

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Visual object tracking, pivotal for applications like earth observation and environmental monitoring, encounters challenges under adverse conditions such as low light and complex backgrounds. Traditional tracking technologies often falter, especially when tracking dynamic objects like aircraft amidst rapid movements and environmental disturbances. This study introduces an innovative adaptive multimodal image object-tracking model that harnesses the capabilities of multispectral image sensors, combining infrared and visible light imagery to significantly enhance tracking accuracy and robustness. By employing the advanced vision transformer architecture and integrating token spatial filtering (TSF) and crossmodal compensation (CMC), our model dynamically adjusts to diverse tracking scenarios. Comprehensive experiments conducted on a private dataset and various public datasets demonstrate the model’s superior performance under extreme conditions, affirming its adaptability to rapid environmental changes and sensor limitations. This research not only advances visual tracking technology but also offers extensive insights into multisource image fusion and adaptive tracking strategies, establishing a robust foundation for future enhancements in sensor-based tracking systems.

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49

Ruiz-Alzola, Juan, Carlos Alberola-López, and Jose-Ramón Casar Corredera. "Model-based stereo-visual tracking: Covariance analysis and tracking schemes." Signal Processing 80, no. 1 (January 2000): 23–43. http://dx.doi.org/10.1016/s0165-1684(99)00109-7.

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50

Beutter, B. R., J. Lorenceau, and L. S. Stone. "Visual Coherence Affects Smooth Pursuit." Perception 25, no. 1_suppl (August 1996): 10. http://dx.doi.org/10.1068/v96l0202.

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For four subjects (one naive), we measured pursuit of a line-figure diamond moving along an elliptical path behind an invisible X-shaped aperture under two conditions. The diamond's corners were occluded and only four moving line segments were visible over the background (38 cd m−2). At low segment luminance (44 cd m−2), the percept is largely a coherently moving diamond. At high luminance (108 cd m−2), the percept is largely four independently moving segments. Along with this perceptual effect, there were parallel changes in pursuit. In the low-contrast condition, pursuit was more related to object motion. A \chi2 analysis showed ( p>0.05) that for 98% of trials subjects were more likely tracking the object than the segments, for 29% of trials one could not reject the hypothesis that subjects were tracking the object and not the segments, and for 100% of trials one could reject the hypothesis that subjects were tracking the segments and not the object. Conversely, in the high-contrast condition, pursuit appeared more related to segment motion. For 66% of trials subjects were more likely tracking the segments than the object; for 94% of trials one could reject the hypothesis that subjects were tracking the object and not the segments; and for 13% of trials one could not reject the hypothesis that subjects were tracking the segments and not the object. These results suggest that pursuit is driven by the same object-motion signal as perception, rather than by simple retinal image motion.

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