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DRIVING INNOVATION: UNVEILING THE CENTROG FEATURE TECHNIQUE FOR VEHICLE PERFORMANCE

Abubakar Bankole , Faculty of Science, Department of Computer Science, Nigerian Defence Academy, Kaduna, Nigeria

Abstract

This paper introduces the Centrog Feature Technique, a groundbreaking innovation in the realm of vehicle performance enhancement. The Centrog Feature Technique employs cutting-edge technology to revolutionize various aspects of vehicle functionality, from efficiency to safety and beyond. This abstract provides an overview of the technique's key components and its potential impact on the automotive industry.

Keywords

Centrog Feature Technique, vehicle performance, innovation

References

Martins E Irhebhude, Mohammad Athar Ali, and Eran A Edirisinghe. Pedestrian detection and vehicle type recognition using centrog features for nighttime thermal images. In Intelligent Computer Communication and Processing (ICCP), 2015 IEEE International Conference on, pages 407–412. IEEE, 2015.

Yoichiro Iwasaki, Masato Misumi, and Toshiyuki Nakamiya. Robust vehicle detection under various environmental conditions using an infrared thermal camera and its application to road traffic flow monitoring. Sensors, 13(6):7756– 7773, 2013.

Martins E Irhebhude, Nawahda Amin, and Eran A Edirisinghe. View invariant vehicle type recognition and counting system using multiple features. International Journal of Computer Vision and Signal Processing, 6(1): 20-32, 2016.

Khairi Abdulrahim and Rosalina Abdul Salam. Traffic surveillance: A review of vision based vehicle detection, recognition and tracking. International Journal of Applied Engineering Research, 11(1):713–726, 2016.

Noppakun Boonsim and Simant Prakoonwit. Car make and model recognition under limited lighting conditions at night. Pattern Analysis and Applications, pages 1–13, 2016.

Jakub Sochor, Adam Herout, and Jiri Havel. Boxcars: 3d boxes as cnn input for improved fine- grained vehicle recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3006–3015, 2016.

Jhonghyun An, Baehoon Choi, Kwee-Bo Sim, and Euntai Kim. Novel intersection type recognition for autonomous vehicles using a multi-layer laser scanner. Sensors, 16(7):1123, 2016.

Heikki Huttunen, Fatemeh Shokrollahi Yancheshmeh, and Ke Chen. Car type recognition with deep neural networks. arXiv preprint arXiv:1602.07125, 2016.

Ye Li, Bo Li, Bin Tian, and Qingming Yao. Vehicle detection based on the and-or graph for congested traffic conditions. Intelligent Transportation Systems, IEEE Transactions on, 14(2):984– 993, 2013.

Ehsan Adeli Mosabbeb, Maryam Sadeghi, and Mahmoud Fathy. A new approach for vehicle detection in congested traffic scenes based on strong shadow segmentation. In Advances in Visual Computing, pages 427–436. Springer, 2007.

Ming Yin, Hao Zhang, Huadong Meng, and Xiqin Wang. An hmm-based algorithm for vehicle detection in congested traffic situations. In Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE, pages 736–741. IEEE, 2007.

S. Gupte, O. Masoud, R.F.K. Martin, and N.P. Papanikolopoulos. Detection and classification of vehicles. Intelligent Transportation Systems, IEEE Transactions on, 3(1):37–47, 2002.

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DRIVING INNOVATION: UNVEILING THE CENTROG FEATURE TECHNIQUE FOR VEHICLE PERFORMANCE. (2024). International Journal of Artificial Intelligence, 4(02), 01-05. https://www.academicpublishers.org/journals/index.php/ijai/article/view/268