# A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning
## 3 Methods
### DLA-Efficient
To address these shortcomings and further refine the algorithm, future research can explore the following avenues: integrating multimodal data, including radar, lidar, and other sensor inputs, to bolster detection and tracking capabilities in complex environments; implementing dynamic weight adjustment to enhance adaptability by dynamically tuning the weight of each loss function in response to real-time environmental variations; incorporating an online learning and self-adaptive mechanism to continually optimize the algorithm post-deployment, thereby improving long-term tracking accuracy; leveraging contextual information integration by incorporating road conditions and traffic rules to strengthen the algorithm's robustness across diverse scenarios; and developing advanced occlusion handling techniques to ensure high-precision tracking even during extended periods of occlusion. By implementing these enhancements, the FairMOT-MCVT algorithm is anticipated to demonstrate greater robustness and accuracy in more intricate and dynamic traffic environments, ultimately providing more reliable technical underpinnings for intelligent transportation systems. Conceptualization, M.L. ([PERSON]) and W.Z. ; methodology, M.L. ([PERSON]); software, M.L. ([PERSON]); validation, M.L. ([PERSON]), W.Z., M.L. ([PERSON]) and C.Z. ; formal analysis, M.L. ([PERSON]) and C.Z. ; investigation, M.L. ([PERSON]) and E.C. ; resources, M.L. ([PERSON]); data curation, M.L. ([PERSON]); writing--original draft preparation, M.L. ([PERSON]); writing--review and editing, E.C. ; supervision, W.G. ; project administration, M.L. ([PERSON]); funding acquisition, W.Z. and W.G. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Shanghai Special Funds for Centralized Guided Local Science and Technology Development (YDZX20233100002002) and supported by the Postdoctoral Fellowship Program of CPSF under Grant Number GZB20240351. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in the study are available on request from the corresponding author. The data are not publicly available due to privacy. **Conflicts of Interest:** Author [PERSON] was employed by the company Shanghai Smart Vehicle Cooperating Innovation Center Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Abbreviations

* [1]MOT Multi-object Tracking IDF1 IDF1 Score FPS Frames Per Second UA-DETRAC University of Alberta-Detection and Tracking of Vehicles MSDA Multi-scale Dilated Attention MOTA Multiple Object Tracking Accuracy FP False Positive FN False Negative IDTP ID True Positive IDFP ID False Positive IDFN ID False Negative GT Ground Truth DLA Deep Layer Aggregation Re-ID Re-identification LIDAR Light Detection and Ranging YOLO You Only Look Once SSD Single Shot Multi-box Detector JPDA Joint Probabilistic Data Association MHT Multiple Hypothesis Tracking MCHS Multi-camera Highway Scenario CNN Convolutional Neural Network R-CNN Region-based Convolutional Neural Network SORT Simple Online and Realtime Tracking DeepSORT Deep Simple Online and Realtime Tracking JDE Jointly Learns the Detector and Embedding Model CenterNet Center-based Object Detection Network Deeply separable convolution is an approach to optimizing convolutional neural networks that decomposes standard convolution into two simpler operations: Separable perdurbwise convolution and pointwise convolution. This approach significantly reduces the amount of computation and number of parameters while maintaining the performance of the convolutional neural network. * [2]Multi-scale feature fusion is an approach that combines feature maps at different scales to utilize features at different scales to improve detection and classification accuracy.