2026· ITEGAM- Journal of Engineering and Technology for Industrial Applications (ITEGAM-JETIA)· Vol 12, pp. 1290-1299· 0 citations
TL;DR
A deep learning-based car surveillance system, which combines the YOLOv8 object detector and a multi-object tracking system to perform automated automobile detection and tracking, and results support the efficiency of the methodology.
Abstract
Intelligent transportation systems, traffic surveillance and smart city monitoring require accurate vehicle detection and tracking. Traditional methods of monitoring are usually based on either manual monitoring or GPS-based monitoring which may have issues with signal dependency and lack of scalability. This paper suggests a deep learning-based car surveillance system, which combines the YOLOv8 object detector and a multi-object tracking system to perform automated automobile detection and tracking. A prepared set of custom traffic image data (about 1,700 images) was divided into a training (80%) and validation (20%) sample and trained on the YOLOv8-Nano model to detect vehicles. Images had been resized to 640 × 640 resolution during training with 50 epochs of a batch size of 16 with transfer learning on pretrained weights. The trained detector had the accuracy of 0.81 with the recall of 0.74 and the mean Average Precision (mAP-0.5) of 0.79 on vehicle detection assignments. The detecting unit was further combined with tracking structure to retain vehicle identities between sequential frames to provide the capability of regular tracking of multi-object in traffic environment. The test outcomes show that the suggested system can work at about 40 frames per seconds (FPS) with the evaluation dataset and still retain a good tracking precision of about 0.76. Bounding boxes, tracking IDs and performance graphs are some of the visualization results, which support the efficiency of the methodology. The given framework can also be used to track the location of military vehicles in surveillance domains during the situations when the convoy movements or tactical vehicle location can be monitored automatically in GPS-denied or irregular conditions.
The installation of a real-time visual tracking system with an active pan-tilt camera for indoor human motion detection is presented, which shows that the inclusion of YOLOv10 significantly improves detection precision and temporal consistency.
Ayman Javid Hussain, Lalitha Saroja Ch, Ruqiya Fatima· International Journal of AI...· 0 citations
Vehicle detection and tracking in unmanned aerial vehicle (UAV) imagery, while critical for intelligent transportation systems, remain challenging due to high omission rates, false alarms, and frequent identity switches among small-sized vehicles. The proposed research establishes an enhanced tracking-by-detection framework integrating an improved YOLOX with DeepSORT to mitigate the aforementioned challenges. Integrating the Convolutional Block Attention Module (CBAM) into the feature fusion stage optimizes feature extraction within complex environments. Furthermore, the detection head is refined by replacing standard Binary Cross-Entropy (BCE) and IoU losses with Varifocal Loss and Enhanced IoU (EIoU) loss, respectively, to mitigate sample imbalance and boost localization accuracy. A sliding-window-based image slicing method is also introduced to enhance detection sensitivity to small-sized spatial features. Experimental results demonstrate that the proposed method significantly reduces detection errors and identity switches while strengthening tracking stability.
Jianping Zeng, Jiang-Hong Zhu· International Conference on...· 0 citations
Vehicle recognition is a critical component of traffic analysis and the progress of advanced transportation systems, underscoring the importance of automated, real-time methods that reduce the need for manual observation. While the field has seen notable innovations in deep learning-centric detection technologies, many of these approaches require considerable computational strength and are not well-suited for real-time application in resource-constrained environments. In response to this limitation, the present study introduces a streamlined vehicle detection framework that combines background subtraction for motion-oriented foreground extraction with a Haar cascade classifier for object identification in traffic video sequences. The system is evaluated using real-world highway traffic recordings under different illumination conditions, including both day and night scenarios. The experiment's findings show that the system achieves an overall accuracy of 82.08%, with a precision of 85.33%, a recall of 66.67%, and an F1-score of 74.86%. The system also demonstrates consistent performance across different lighting conditions. These findings indicate a trade-off between detection accuracy and computational efficiency, where the proposed approach prioritizes practical deployment feasibility. Overall, the results suggest that classical computer vision techniques remain viable alternatives for real-time traffic monitoring in environments with limited computational resources.
Ni Gusti Ayu Dasriani, Anthony Anggrawan, Khasnur Hidjah et al.· International Journal of Inf...· 0 citations
Traditional traffic target detection heavily relies on manual processing. However, the latest advancements in deep learning have significantly enhanced the capabilities of target detection and multi-target tracking. To address these challenges, this paper proposes a perception-tracking-reasoning framework based on traffic rules, which is used for vehicle recognition and driving-state analysis in surveillance videos. This framework integrates enhanced vehicle perception, cross-frame identity association, trajectory-state modeling, and interpretable rule reasoning into a unified processing flow. Finally, experiments show that the main advantage of the proposed model lies in its ability to detect small-sized vehicle targets and improve trajectory stability in complex traffic scenarios.
Shu-Jing Xie, Zhi-Hao Zhang, Shuo Wang et al.· Applied Sciences· 0 citations
The proposed work introduces adaptive frame slicing scheme in the input data loader and geometric positional encoding which enables the detection of faraway vehicles with high accuracy in wide area surveillance imagery.
M. Ilamathi, Sabitha Ramakrishnan· Indian Journal of Science an...· 0 citations
Fixed-time traffic signals cannot respond to short-term changes in vehicle demand and may allocate green time inefficiently at urban intersections. This study developed and evaluated a YOLOv8-based vehicle-detection and traffic-density estimation prototype for adaptive signal applications. Two public traffic-intersection datasets supplied 3,300 images, of which 2,932 were retained after quality screening. Images were resized to 640 × 640 pixels, annotated with vehicle bounding boxes, and augmented through ±25% brightness and saturation adjustments. YOLOv8 was trained for 50 epochs using 70:30, 80:20, and 90:10 training-testing splits and was compared with Faster R-CNN under the 70:30 configuration. YOLOv8 achieved mAP@0.5 values of 0.989–0.992 and mAP@0.5:0.95 values of 0.962–0.965. Under the direct comparison, YOLOv8 obtained 0.989 mAP@0.5, 0.962 mAP@0.5:0.95, and 0.980 recall, while requiring 1.9 ms per image compared with 28.4 ms for Faster R-CNN. The detector was integrated into a web prototype that displayed vehicle counts, estimated density, and signal duration. The results support YOLOv8 as an efficient traffic-monitoring component, although density-classification accuracy and traffic-flow improvement require separate validation.
Unknown authors· International journal of re...· 0 citations
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