Moving object detection and tracking using deep learning neural network and correlation filter

Supreeth, H S G; Patil, Chandrashekar M

doi:10.1109/icicct.2018.8473354

Cited by 11 publications

(1 citation statement)

References 20 publications

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“…Mirthubashini and Santhi (2020) surveyed various vehicle detection and tracking techniques, including both deep-learning and classical detection and tracking methods, such as Gaussian mixture models (GMM) (Supreeth and Patil 2018) and Mixture of Gaussians (MoG) + Support Vector Machines (SVM) (Arinaldi et al 2018). They found that deep-learning techniques for object detection have an advantage compared to conventional image processing techniques.…”

Section: Counting By Regressionmentioning

confidence: 99%

Efficient City-Wide Multi-Class Multi-Movement Vehicle Counting: A Survey

Anastasiu

Gaul

Vazhaeparambil³

et al. 2020

J. Big Data Anal. Transp.

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With the advent of accurate deep learning-based object detection methods, it is now possible to employ prevalent city-wide traffic and intersection cameras to derive actionable insights for improving traffic, road infrastructure, and transit. A crucial tool in signal timing planning is capturing accurate movement-and class-specific vehicle counts. To be useful in online intelligent transportation systems, methods designed for this task must not only be accurate in their counting, but should also be efficient. In this paper, we study the multi-class multi-movement vehicle counting problem, overview the state-of-the-art methods designed to solve this problem, and present a series of comprehensive experiments, using traffic footage with O(5) vehicles captured from 20 different vantage points and covering various lighting and weather conditions. Our survey aims to answer the question whether we are ready to leverage traffic cameras for real-time automatic vehicle counting. The results of our analysis show several promising approaches and identify areas where additional improvement is needed.

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Section: Counting By Regressionmentioning

confidence: 99%