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Title	Deep Learning–Based Vehicle Counting And Speed Estimation In Surveillance Videos Using Object Detection
ID_Doc	17986
Authors	Saxena G.; Paraye A.; Verma D.K.; Rajan A.; Verma R.K.
Year	2025
Published	Proceedings - 3rd International Conference on Advancement in Computation and Computer Technologies, InCACCT 2025
DOI	http://dx.doi.org/10.1109/InCACCT65424.2025.11011367
Abstract	This paper introduces a Deep Learning–based Vehicle Counting and Speed Estimation (VCSE) system. The proposed VCSE system applies object detection techniques and custom-developed algorithms on surveillance videos. For object detection, the system uses the Single-Shot Detection algorithm and fine-tuned MobileNetV2 CNN over a custom dataset. Moreover, it uses a custom-developed algorithm for object tracking and speed estimation. After fine-tuning object detection model, the mAP score for object detection was 0.88. The parameters of the speed estimation algorithm were also fine-tuned using a curated calibration dataset that contains videos of the vehicles with varying direction, speed and environment. The accuracy of the speed estimation was around 98% after calibration. The proposed VCSE system finds its applications in law enforcement, smart-city planning and traffic management. It is a valuable tool for enhancing the safety and security posture of an organization by monitoring the vehicles’ speed on a real-time and 24/7 basis. © 2025 IEEE.
Author Keywords	convolution neural network; Object detection; object tracking; road safety; surveillance videos; vehicle counting; vehicle overspeed detection

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