Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras
Author:
Kalake LesoleORCID, Wan WanggenORCID, Dong YanqiuORCID
Abstract
The increase in security threats and a huge demand for smart transportation applications for vehicle identification and tracking with multiple non-overlapping cameras have gained a lot of attention. Moreover, extracting meaningful and semantic vehicle information has become an adventurous task, with frameworks deployed on different domains to scan features independently. Furthermore, approach identification and tracking processes have largely relied on one or two vehicle characteristics. They have managed to achieve a high detection quality rate and accuracy using Inception ResNet and pre-trained models but have had limitations on handling moving vehicle classes and were not suitable for real-time tracking. Additionally, the complexity and diverse characteristics of vehicles made the algorithms impossible to efficiently distinguish and match vehicle tracklets across non-overlapping cameras. Therefore, to disambiguate these features, we propose to implement a Ternion stream deep convolutional neural network (TSDCNN) over non-overlapping cameras and combine all key vehicle features such as shape, license plate number, and optical character recognition (OCR). Then jointly investigate the strategic analysis of visual vehicle information to find and identify vehicles in multiple non-overlapping views of algorithms. As a result, the proposed algorithm improved the recognition quality rate and recorded a remarkable overall performance, outperforming the current online state-of-the-art paradigm by 0.28% and 1.70%, respectively, on vehicle rear view (VRV) and Veri776 datasets.
Funder
Shanghai Science and Technology Committee International Cooperation China Postdoctoral Science Foundation
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference38 articles.
1. Scheel, A., Knill, C., Reuter, S., and Dietmayer, K. (2016, January 19–22). Multi-sensor multi-object tracking of vehicles using high-resolution radars. Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden. 2. Bai, A. (2020, August 14). Multi-Object Tracking and Identification over Sets. Available online: http://arxiv.org/abs/1605.07960. 3. Wang, J., Zeng, X., Luo, W., and An, W. (2018). The Application of Neural Network in Multiple Object Tracking. DEStech Trans. Comput. Sci. Eng., 258–264. 4. Marin-Reyes, P.A., Bergamini, L., Lorenzo-Navarro, J., Palazzi, A., Calderara, S., and Cucchiara, R. (2018, January 18–23). Unsupervised vehicle re-identification using triplet networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA. 5. Kim, T., Yun, B., Kim, T., and Lee, J. (2021). Recognition of Vehicle License Plates Based on Image Processing. Appl. Sci., 11.
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