The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.
Tracking-Learning-Detection
Abstract
This paper investigates long-term tracking of unknown objects in a video stream. The object is defined by its location and extent in a single frame. In every frame that follows, the task is to determine the object's location and extent or indicate that the object is not present. We propose a novel tracking framework (TLD) that explicitly decomposes the long-term tracking task into tracking, learning, and detection. The tracker follows the object from frame to frame. The detector localizes all appearances that have been observed so far and corrects the tracker if necessary. The learning estimates the detector's errors and updates it to avoid these errors in the future. We study how to identify the detector's errors and learn from them. We develop a novel learning method (P-N learning) which estimates the errors by a pair of "experts": (1) P-expert estimates missed detections, and (2) N-expert estimates false alarms. The learning process is modeled as a discrete dynamical system and the conditions under which the learning guarantees improvement are found. We describe our real-time implementation of the TLD framework and the P-N learning. We carry out an extensive quantitative evaluation which shows a significant improvement over state-of-the-art approaches.
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Fully-Convolutional Siamese Networks for Object Tracking
Bertinetto, L., Valmadre, J., Henriques, J. F., Vedaldi, A., & Torr, P. H. S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-48881-3_56 · Cited by 4,546
Learning Multi-domain Convolutional Neural Networks for Visual Tracking
Nam, H., & Han, B. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.465 · Cited by 2,806
A Benchmark and Simulator for UAV Tracking
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LaSOT: A High-Quality Benchmark for Large-Scale Single Object Tracking
Fan, H., Lin, L., Yang, F., Chu, P., Deng, G., Yu, S., Bai, H., Xu, Y., Liao, C., & Ling, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00552 · Cited by 1,742
“…techniques ( e.g ., BACF [15], SRDCF [10], CSRDCF [36], Staple CA [40] and STRCF [29]), ensemble trackers ( e.g ., PTAV [13], LCT [38], MEEM [56] and TLD [24]), sparse trackers ( e.g ., L1APG [2] and ASLA [23]), other representatives ( e.g ., CT [58], IVT [44], MIL [1] and Struck [17]).”
Method
Metadata
- Authors
- Kalal, Z., Mikolajczyk, K., & Matas, J.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2012-07
- Volume
- 34
- Issue
- 7
- Pages
- 1409-1422
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2124211486
- Cited by
- 3,285
- References
- 78
- FWCI
- 138.02
- Type
- journal article
- Language
- en
- Open access
- closed
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