Relevant bibliographies by topics / Camshift algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Camshift algorithm'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Camshift algorithm"

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Tang, Quan, Shu Guang Dai, and Jie Yang. "Object Tracking Algorithm Based on Camshift Combining Background Subtraction with Three Frame Difference." Applied Mechanics and Materials 373-375 (August 2013): 1116–19. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1116.

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Camshift tracking algorithm is based on probability distribution of color , it is susceptible to be interfered by the same color in the background, which will lead to the failure of the target tracking. To overcome this problem it presented an improved Camshift tracking algorithm. It combined background subtraction method with three frame difference method to detect target, got rectangular characteristic parameters of the motion target area as the Camshift initialization parameters, replaced the general Camshift algorithm which is based on color feature. Experimental results show that Camshift
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Huang, Yi Hu, Ji Xiang Ma, Xiao Dong Han, Ning Hu, and Xi Mei Jia. "Design of Human Tracking Algorithm Based on Improved Camshift." Key Engineering Materials 561 (July 2013): 677–82. http://dx.doi.org/10.4028/www.scientific.net/kem.561.677.

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Considering the problem of added background interference when initial target region is chose in Camshift tracking algorithm. This paper proposes a human tracking algorithm based on improved Camshift. The algorithm uses weight to determine the type of pixels in the back-projection, and then convert the back-projection into binary image, so as to improve the input accuracy of Camshift processing function. According to the human body size information, the algorithm appropriately improves the ratio of major axis and minor axis of search window, to optimize the output size of Camshift processing fu
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Guo, Cheng Yi, and Wen Bing Fan. "Research on Application of Camshift and Kalman Filter Algorithm in Video Object Tracking." Advanced Materials Research 1049-1050 (October 2014): 1685–89. http://dx.doi.org/10.4028/www.scientific.net/amr.1049-1050.1685.

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When the background is complex and there is a lot color similar pixel interference, it may lead to location and size of Camshift algorithm’s search window abnormal so as to tracking failure. Aiming at these problems, this paper proposed a algorithm that combinating Camshift algorithm and Kalman filter. Kalman filter can predict the position of the moving object. Camshift algorithm adjusted the position and size of search window by using the prediction, so as to ensure the correct operation of the Camshift algorithm. Experimental results show that the proposed algorithm can effectively overcome
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Zhang, Yuanyuan, Xiaomei Zhao, Fengjiao Li, Jiande Sun, Shuming Jiang, and Changying Chen. "Robust Object Tracking Based on Simplified Codebook Masked Camshift Algorithm." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/376494.

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Moving targets detection and tracking is an important and basic issue in the field of intelligent video surveillance. The classical Codebook algorithm is simplified in this paper by introducing the average intensity into the Codebook model instead of the original minimal and maximal intensities. And a hierarchical matching method between the current pixel and codeword is also proposed according to the average intensity in the high and low intensity areas, respectively. Based on the simplified Codebook algorithm, this paper then proposes a robust object tracking algorithm called Simplified Code
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Ban, Li Ying, Yue Hua Han, and Yan Hai Wu. "Target Tracking Based on Improved Camshift and Kalman Filter." Advanced Materials Research 989-994 (July 2014): 3587–90. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3587.

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A tracking algorithm based on improved Camshift and Kalman filter is proposed in this paper to deal with the problems in traditional Camshift algorithm, such as tracking failure under color interference or occlusion. Firstly, the proposed algorithm improves the single color target model and presents a novel target model, which fuses color and motion cues, to enhance the robustness and accuracy of target tracking. And in order to increase the tracking efficiency, the algorithm combines Kalman filter with the improved Camshift algorithm by using Kalman filter to predict the position of the track
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Xiong, Yuan Yi, Jie Yang, and Chuan Wang. "Human Motion Tracking and Alarm System Based on DaVinci." Applied Mechanics and Materials 568-570 (June 2014): 647–51. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.647.

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The paper proposes a improved Camshift algorithm which solve the problem of the original Camshift that have limitations when the tracking target have similar color with the background and is obstructed. The paper combines codebook model with the Camshift. The YUV space is used in foreground detection rather than the RGB. The results of experiments show that the algorithm works well in complex background, occlusion and the same color interference. At last we achieve a warning system.
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Bankar, Rushikesh Tukaram, and Suresh Salankar. "The Comparative Analysis of a Vision Based HGR System Used for Handicapped People." European Journal of Engineering Research and Science 4, no. 10 (2019): 52–54. http://dx.doi.org/10.24018/ejers.2019.4.10.1509.

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The object tracking is critical to visual / video surveillance, analysis of the activity and gesture recognition. The major difficulties to be occurred in the visual tracking are different environmental conditions, illumination changes, occlusion and appearance. In this paper, the comparative analysis of the different systems which are used to recognize the head gestures under different environmental conditions is discussed. The existing algorithm used to recognize the head gestures has some limitations. The existing algorithm cannot work under outdoor environmental conditions. The traditional
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Bankar, Rushikesh Tukaram, and Suresh Salankar. "Comparative Analysis of a Vision Based HGR System Used for Handicapped People." European Journal of Engineering and Technology Research 4, no. 10 (2019): 52–54. http://dx.doi.org/10.24018/ejeng.2019.4.10.1509.

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The object tracking is critical to visual / video surveillance, analysis of the activity and gesture recognition. The major difficulties to be occurred in the visual tracking are different environmental conditions, illumination changes, occlusion and appearance. In this paper, the comparative analysis of the different systems which are used to recognize the head gestures under different environmental conditions is discussed. The existing algorithm used to recognize the head gestures has some limitations. The existing algorithm cannot work under outdoor environmental conditions. The traditional
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Zhang, Zheng, Cong Huang, Fei Zhong, Bote Qi, and Binghong Gao. "Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision." Complexity 2021 (May 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/5526831.

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This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an
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Purba, Ronsen, Irpan Adiputra Pardosi, Feredy Lestari Pandia, and Yudi Pratama Hasibuan. "Moving Object Tracking Using CAMSHIFT and SURF Algorithm." Jurnal SIFO Mikroskil 16, no. 1 (2015): 103–12. http://dx.doi.org/10.55601/jsm.v16i1.184.

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Penjejakan objek bergerak (moving object tracking) sebagai sebuah permasalahan yang berperan penting dalam bidang computer vision dan secara luas dapat diterapkan dalam banyak aplikasi dunia nyata seperti pengawasan otomatis, human pose estimation, navigasi kendaraan, pemantauan lalu lintas, dan robot vision. Moving object tracking membutuhkan metode yang memiliki akurasi dan ketahanan yang baik terhadap perubahan yang terjadi pada objek. ??? Penelitian ini membangun sebuah aplikasi untuk membandingkan Algoritma Camshift (Continuosly Adaptive Mean-Shift) dan Algoritma SURF (Speeded Up Robust F
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Dissertations / Theses on the topic "Camshift algorithm"

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Klvaňa, Marek. "Sledování vybraného objektu v dynamickém obraze." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-229705.

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The aim of this thesis is a description and implementation of algorithms of the tracked objects in the video feed. This thesis introduces Mean shift and Continuously adaptive mean shift algorithms which represent category based on kernel tracking. For construction of a model is used a threedimensional color histogram whose construction is described in this thesis as well. The achievements of described algorithms are compared in the testing images sequences and evaluated in details.
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Brunclík, Robert. "Automatická regulace velikosti písma podle vzdálenosti čtenáře." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241995.

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The thesis deals with automatic control the font size by the distance from the reader. It includes theoretical acquaintance with the face detection and subsequent tracking of the detected area during the scene. Furthermore, there is a comparison of the tracking algorithms. Then the calculation of distance is decribed. It is based on the user’s calibration and based on the outcome occurs the font size is automatically corrected. There is also a description of a separate application Automatical controller of the text size, with the recommended settings of the program.
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Tokatli, Aykut. "3d Hand Tracking In Video Sequences." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606461/index.pdf.

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The use of hand gestures provides an attractive alternative to cumbersome interface devices such as keyboard, mouse, joystick, etc. Hand tracking has a great potential as a tool for better human-computer interaction by means of communication in a more natural and articulate way. This has motivated a very active research area concerned with computer vision-based analysis and interpretation of hand gestures and hand tracking. In this study, a real-time hand tracking system is developed. Mainly, it is image-based hand tracking and based on 2D image information. For separation and identification o
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Thomas, George L. "Biogeography-Based Optimization of a Variable Camshaft Timing System." Cleveland State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=csu1419775790.

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Chou, Shu-Wei, and 周書暐. "A Modified CamShift Algorithm for Video Object Tracking." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/84382736893253456423.

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碩士<br>銘傳大學<br>資訊傳播工程學系碩士班<br>102<br>Mean-shift algorithm is a popular and high efficient object tracking method. The CamShift is an adaptive version of Mean-Shift algorithm. It is able to adjust the size of object window automatically. CamShift employs the histogram of the tracking target to generate the probability map and then find new center accordingly. Camshift has wide attention for objet tracking because of its high efficiency and robustness. However, it is often distracted or interfered by the other larger objects with similar colors. This paper presents a novel tracking algorithm base
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JIAN, MU-JHE, and 簡睦哲. "Improved Camshift Algorithm for Tracking Objects in Complex Environments." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/xd7tq4.

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碩士<br>國立宜蘭大學<br>電機工程學系碩士班<br>107<br>The main research in this paper is to improve the traditional algorithm of Camshift. In the traditional Camshift algorithm, the target can be tracked in a simple environment with high efficiency and high accuracy, and tracked according to the color probability distribution of the target, although in a simple environment. The tracking effect is good, but the environmental changes are always changing rapidly. In the process of tracking the target, if it occurs in a similar color background, the target is obscured by other obstacles, or the light changes, etc.,
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Huang, Chun-lin, and 黃俊霖. "Design of Real-Time Object Tracking System Using CamShift Algorithm." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/08498874349723356589.

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碩士<br>國立成功大學<br>工程科學系碩博士班<br>97<br>Vision was the most advanced sense of human, and it holds a very responsible sense for image capture. Computer techno is moving forward, the technical innovations in image process was maturity, but not completed. In convention, most image target detecting and tracking was based on build-in video camera. Unfortunately, it will inactive when the target out of the shoot. This thesis addressed one real-time target tracking system methodology for improving the tracking dead-zone. Whole system integrates embedded USB 2.0 engine 8051-based MCU, PAS6311LT CMOS image
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Haung, Wei-Tao, and 黃韋韜. "Realization of Moving Platform Control for a Landing RotorCraft Based on Camshift Algorithm." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/z78r5d.

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碩士<br>國立東華大學<br>電機工程學系<br>104<br>With the advance of autonomy technology, quadrotor crafts have found their applications in a variety of fields, including rescue-aid, military, entertainment, and secure monitoring etc. Among others, auto-landing is one of key technical issues for quadrotors. Unfortunately, landing with the aid of GPS unavoidably bring about error in accuracy. The thesis endeavor to develop an autonomous mobile platform to pick up the landing rotorcraft using image processing and visual servoing techniques. The autonomous platform is driven by omnidirectional wheels and can be
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Liou, Yun-Jung, and 劉允中. "An Improved CamShift Algorithm Based on Adaptive Motion Estimation for Multiple Camera Systems." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/78721416340903036566.

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碩士<br>淡江大學<br>電機工程學系碩士班<br>100<br>Smart video surveillance has been developed for a long time, and many approaches to track moving objects have been proposed in recent years. The research of good tracking algorithms becomes one of the main streams for the smart video surveillance research. Multiple moving object tracking is a fundamental task on smart video surveillance systems, because it provides a focus of attention for further investigation. Video surveillance using multiple cameras system has attracted increasing interest in recent years. Moving objects occlusion is a key operation using
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Book chapters on the topic "Camshift algorithm"

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Sooksatra, Sorn, and Toshiaki Kondo. "CAMSHIFT-Based Algorithm for Multiple Object Tracking." In The 9th International Conference on Computing and InformationTechnology (IC2IT2013). Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37371-8_33.

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Tang, Bo, Zouyu Xie, and Liufen Li. "Improved Camshift Tracking Algorithm Based on Color Recognition." In 2021 International Conference on Applications and Techniques in Cyber Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79197-1_74.

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Guo, Yanming, Songyang Lao, and Liang Bai. "Player Detection Algorithm Based on Color Segmentation and Improved CamShift Algorithm." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34528-9_80.

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Wang, Jun, Jin-ye Peng, Xiao-yi Feng, Lin-qing Li, and Dan-jiao Li. "An Improved Camshift-Based Particle Filter Algorithm for Face Tracking." In Intelligent Science and Intelligent Data Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31919-8_36.

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Chen, Kun, ChunLei Liu, and Yongjin Xu. "Face Detection and Tracking Based on Adaboost CamShift and Kalman Filter Algorithm." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45261-5_16.

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Tian, Yun, Carol Taylor, and Yanqing Ji. "Improving the Performance of the CamShift Algorithm Using Dynamic Parallelism on GPU." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54978-1_84.

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Sharma, Prateek, Pranjali M. Kokare, and Maheshkumar H. Kolekar. "Performance Comparison of KLT and CAMSHIFT Algorithms for Video Object Tracking." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2685-1_31.

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Gupta, Smriti, Kundan Kumar, Sabita Pal, and Kuntal Ghosh. "A Comprehensive Study of MeanShift and CamShift Algorithms for Real-Time Face Tracking." In Smart Innovation, Systems and Technologies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5971-6_86.

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Yang, Yueting, Shaolin Hu, Guogang Wang, and Ye Ke. "Moving Object Detection and Tracking in Video Frame Based on OpenCV." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230837.

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Local occlusion may be developed during the target motion, such that it is urgent to solve the problem of video tracking loss caused by moving target occlusion. In this paper, the computer vision library OpenCV is used to preprocess the motion video frame. Two algorithms are combined to solve the problem of tracking loss due to target-background similarity and occlusion: one is the Camshift algorithm (which is used to track the moving target); the other is the Kalman filter (which is used to predict the target position). Comparing with Meanshift algorithms in the same experimental environment,
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"An improved Camshift algorithm based on occlusion and scale variation." In Information Technology and Computer Application Engineering. CRC Press, 2013. http://dx.doi.org/10.1201/b15936-137.

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Conference papers on the topic "Camshift algorithm"

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Tian, Feng, Shujing Zhang, and Dan Pan. "Design and Implementation of Three Frame Difference Method and Camshift Algorithm Motion Object Detection System Based on FPGA." In 2025 10th International Conference on Computer and Communication System (ICCCS). IEEE, 2025. https://doi.org/10.1109/icccs65393.2025.11069831.

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Nadgeri, S. M., S. D. Sawarkar, and A. D. Gawande. "Hand Gesture Recognition Using CAMSHIFT Algorithm." In Third International Conference on Emerging Trends in Engineering and Technology (ICETET 2010). IEEE, 2010. http://dx.doi.org/10.1109/icetet.2010.63.

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Shen, Longyun, Weidong Pan, Yinhua Quan, Fajun Chen, and Jin Zheng. "Improved tracking strategy with CamShift algorithm." In 2012 International Conference on Systems and Informatics (ICSAI). IEEE, 2012. http://dx.doi.org/10.1109/icsai.2012.6223452.

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Xiu, Chunbo, Xuemiao Su, and Xiaonan Pan. "Improved target tracking algorithm based on Camshift." In 2018 Chinese Control And Decision Conference (CCDC). IEEE, 2018. http://dx.doi.org/10.1109/ccdc.2018.8407900.

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Fu, Min, Chao Cai, and Yusu Mao. "An improved Camshift algorithm for target recognition." In Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), edited by Nong Sang and Xinjian Chen. SPIE, 2015. http://dx.doi.org/10.1117/12.2203577.

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Kim, Gi-Woo, and Dae-Seong Kang. "Improved CAMshift Algorithm Based on Kalman Filter." In CES-CUBE 2015. Science & Engineering Research Support soCiety, 2015. http://dx.doi.org/10.14257/astl.2015.98.34.

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Liu, Xia, Hongxia Chu, and Pingjun Li. "Research of the Improved Camshift Tracking Algorithm." In 2007 International Conference on Mechatronics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/icma.2007.4303678.

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Huang, Yuangang, Nan Sang, Zongbo Hao, and Wei Jiang. "Eye Tracking Based on Improved CamShift Algorithm." In 2013 6th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2013. http://dx.doi.org/10.1109/iscid.2013.121.

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Yu-Hui Qui, Jian-Wei Zhang, Guang Lin, Yong-Hui Li, and Dong-Fa Gao. "Improved CamShift tracking algorithm based on motion detection." In 2013 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2013. http://dx.doi.org/10.1109/icmlc.2013.6890417.

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Yousf, Wael Mohamed, Osama Mohamed Elmowafy, and Ibrahim Ali Abdl-Dayem. "C18. Modified CAMShift algorithm for adaptive window tracking." In 2012 29th National Radio Science Conference (NRSC). IEEE, 2012. http://dx.doi.org/10.1109/nrsc.2012.6208536.

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