Development of a real-time object-tracking system using Raspberry Pi
209 viewsDOI:
https://doi.org/10.54939/1859-1043.j.mst.90.2023.127-133Keywords:
Object tracking; KCF; Raspberry Pi.Abstract
Object tracking uses computerized algorithms to locate and track targets automatically without human intervention. Applying object-tracking technology to the observation mission will make it more effective and easier. An important requirement was that the tracker must be fast enough to meet the real-time requirements while still ensuring accuracy and stability. In addition, observation equipment usually uses compact hardware (such as embedded computers) and high-resolution cameras. In this paper, a Kernel Correlation Filter (KCF) based tracking algorithm is used with a Raspberry Pi 4B to track objects at sea. The experiment results show that the tracker works well and stably, and the tracking speed reaches 20 FPS with 1280×720 pixels of camera resolution.
References
[1]. T. Collins et al., “Introduction to the Special Section on Video Surveillance”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, 745-746, (2000).
[2]. J. Zhao, M. Wang, “A Study on Missile Plume Tracking and Localizing by Means of Forward Looking Infrared (FLIR)”, Journal of Solid Rocket Technology, Vol. 23, No. 4, 64-68, (2000).
[3]. V. Kastrinaki et al., “A Survey of Video Processing Techniques for Traffic Applications”, Image and Vision Computing, Vol. 21, No. 4, 359-381, (2003).
[4]. F. Bonin-Font et al., “Visual Navigation for Mobile Robots: A Survey”, Journal of Intelligent and Robotic Systems, Vol. 53, No. 3, 263-296, (2008).
[5]. D. Comaniciu et al., “Real-Time Tracking of Non-rigid Objects Using Mean Shift”, Proc of the IEEE CCV and PR, Washington, (2000).
[6]. Y. Cheng, “Mean Shift, Mode Seeking, and Clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 8, 790-799, (1995).
[7]. A. Adam et al., “Robust Fragments-Based Tracking Using the Integral Histogram”, Proc of the IEEE CSCCV and PR, Washington, 798-805, (2006).
[8]. F. Wang et al., “Robust and Efficient Fragments Based Tracking Using Mean Shift”, AEU-International Journal of Electronics and Communications, Vol. 64, No. 7, 614-623, (2010).
[9]. M. Turk, A. Pentland, “Eigenfaces for Recognition”, Journal of Cognitive Neuroscience, Vol. 3, No. 1, 71-86, (1991).
[10]. S. Yan et al., “Graph Embedding and Extensions: A General Framework for Dimensionality Reduction”, IEEE Transactions on PA and MI, Vol. 29, No.1, 40-51, (2007).
[11]. D. Donoho, “Compressed Sensing”, IEEE Transactions on Information Theory, Vol. 52, No.4, 1289-1306, (2006).
[12]. J. Wright, et al, “Sparse Representation for Computer Vision and Pattern Recognition”, IEEE, Vol. 98, No. 6, 1031-1044, (2010).
[13]. D. Bolme, et al., “Visual Object Tracking Using Adaptive Correlation Filter”, Proc of the IEEE CCV and PR, Washington, 2544-2550, (2010).
[14]. J. Henriques et al., “Exploiting the Circulant Structure of Tracking-by-Detection with Kernels”, Proc of the 12th ECCV, Berlin, 702-715, (2012).
[15]. J. Henriques et al., “High-Speed Tracking with Kernelized Correlation Filter”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 3, 583-596, (2014).
[16]. L. Bertinetto, et al., “Staple: Complementary Learners for Real-Time Tracking”, Proc of the IEEE ICCV&PR, Washington, 1401-1409, (2016).
[17]. M. Tang, J. Feng, “Multi-kernel Correlation Filter for Visual Tracking”, Proc of the IEEE ICCV, Washington, 3038-3046, (2016).
[18]. J. Choi et al., “Attentional Correlation Filter Network for Adaptive Visual Tracking”, Proc of the IEEE ICCV&PR, Washington, 4807-4816, (2017).
[19]. C. Ma, et al., “Hierarchical Convolutional Features for Visual Tracking”, Proc of the IEEE ICCV, Washington, 3074-3082, (2015).
[20]. M. Danelljan et al., “Learning Spatially Regularized Correlation Filters for Visual Tracking”, Proc of the IEEE ICCV, Washington, 4310-4318, (2015).
[21]. D. Meimetis, et al., “Real-time multiple object tracking using deep learning methods”, Neural Computing and Applications, Vol. 35, 89-118, (2023).
[22]. Z. Soleimanitaleb, M. A. Keyvanrad, “Single Object Tracking: A Survey of Methods, Datasets, and Evaluation Metrics”, Computer Vision and Pattern Recognition, (2022).
[23]. H. Huadi et al., “Survey of Target Tracking Based on Improved Block Algorithm of Correlation Filtering”, Software Guide, Vol. 22, No. 3, 245-252, (2023).
[24]. H. Grabner, et al., “Real-Time Tracking via On-line Boosting”, Proc of the British Machine Vision Conference, Edinburgh, (2006).
