학술논문

Efficient Visual Tracking Based on Fuzzy Inference for Intelligent Transportation Systems
Document Type
Periodical
Source
IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, IEEE Transactions on. 24(12):15795-15806 Dec, 2023
Subject
Transportation
Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Target tracking
Feature extraction
Visualization
Fuzzy logic
Prediction algorithms
Inference algorithms
Roads
Intelligent transportation systems
remote monitoring
fuzzy inference
pattern matching
visual tracking
Language
ISSN
1524-9050
1558-0016
Abstract
Remote monitoring is an important application of intelligent transportation systems (ITSs). The combination of monitoring equipment and tracking algorithms can be used to automatically track moving targets. The tracking algorithm based on the Siamese network is both accurate and efficient, and its development potential is better than that of other algorithms. Its output is a detection map that reflects the probability that any position in the search area is the center of the target’s bounding box, and the maximum value of the detection map is the center of the target’s bounding box predicted by the algorithm. Owing to partial occlusion, target deformation, out-of-view, and background clutter, local maxima in the detection map may also be the center of the target’s bounding box. A tracker’s ability to make accurate judgments is currently limited. Furthermore, previous trackers extracted only the target features in the initial frame as the matching template. Although this matching template is highly reliable, it cannot effectively combine the target features available in the subsequent frames. Therefore, in this study, fuzzy inference is introduced into the tracking process to analyze the reliability of the detection map. When this map is reliable, the target feature of the search area is transformed into a substitute template; otherwise, multiple substitute templates are selected from the template pool for parallel matching as per the set rules. The optimal result is selected from multiple detection results, based on the priority of the detection results when the initial frame is used as the matching template. Experimental results on multiple datasets show that the proposed algorithm is superior to other similar algorithms in terms of multiple assessment metrics and can improve the robustness of remote monitoring tasks in ITSs.