Open Access
Issue
E3S Web Conf.
Volume 309, 2021
3rd International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2021)
Article Number 01099
Number of page(s) 5
DOI https://doi.org/10.1051/e3sconf/202130901099
Published online 07 October 2021
  1. F. Mehboob, M. Abbas and R. Jiang,”Traffic event detection from road surveillance vide os based on fuzzy logic,” SAI, 2016, doi: 10.1109/SAI. 2016. 7555981. [Google Scholar]
  2. W. Al Okaishi, A. Zaarane, I. Slimani, I. Atouf and M. Benrabh,”A Traffic Surveillance System in Real-Time to Detect and Classify Vehicles by Using Convolutional Neural Network,” SysCoBIoTS, 2019, pp. 1-5. [Google Scholar]
  3. Fedorov, A., Nikolskaia, K., Ivanov, S. et al. Traffic flow estimation with data from a video surveillance camera. J Big Data 6, 73, 2019. [Google Scholar]
  4. K. K. Santhosh, D. P. Dogra, and P. P. Roy. 2020. Anomaly Detection in Road Traffic Using Visual Surveillance: A Survey. ACM Comput. Surv. 53, 6, Article 119, February 2021. [Google Scholar]
  5. B. Tian, B. T. Morris, M. Tang, Y. Liu, Y. Yao, C. Gou, D. Shen, and S. Tang. 2017. Hierarchical and networked vehicle surveillance in ITS: A survey. IEEE Transactions on Intelligent Transportation Systems 18, 1 (2017), 25--48. [Google Scholar]
  6. J, J., R, B., & Al-Heety, A. (2021). Moving vehicle detection from video sequences for Traffic Surveillance System. ITEGAM-JETIA, 7(27), 41-48. [Google Scholar]
  7. R. Babitha Lincy, Gayathri, R. Optimally configured convolutional neural network for Tamil Handwritten Character Recognition by improved lion optimization model. Multimedia Tools and Application (2020). [Google Scholar]
  8. M. Naphade, S. Wang, D. C. Anastasiu, Z. Tang, M. -C. Chang, X. Yang, Y. Yao, L. Zheng, P. Chakraborty, A. Sharma, Q. Feng, V. Ablavsky, and S. Sclaroff,”The 5th ai city challenge,” in The IEEE Conference CVPR Workshops, June 2021. [Google Scholar]
  9. Shuai Bai, Zhedong Zheng, Xiaohan Wang, Junyang Lin, Zhu Zhang, Chang Zhou, Hongxia Yang, and Yi Yang. Connecting language and vision for natural language-based vehicle retrieval. In CVPR Workshop, 2021. [Google Scholar]
  10. Jingyuan Chen, Guanchen Ding, Yuchen Yang, Wenwei Han, Kangmin Xu, Tianyi ao, Zhe Zhang, Wanping Ouyang, Hao Cai, and Zhenzhong Chen. Dual modality vehicle anomaly detection via bidirectional-trajectory tracing. In CVPR Workshop, 2021 [Google Scholar]
  11. Keval Doshi and Yasin Yilmaz. An efficient approach for anomaly detection in traffic videos. In CVPR Workshop, 2021. [Google Scholar]
  12. Qi Feng, Vitaly Ablavsky, and Stan Sclaroff. CityFlow-NL: Tracking and retrieval of vehicles at city scaleby natural language descriptions. arXiv:2101. 04741, 2021. [Google Scholar]
  13. Marta Fernandez, Paula Moral, Alvaro Garcia-Martin, and Jose M. Martinez. Vehicle re-identification based on ensembling deep learning features including a synthetic training dataset, orientation and background features, and camera verification. In CVPR Workshop, 2021. [Google Scholar]
  14. Derek Gloudemans and Daniel B. Work. Fast vehicle turning-movement counting using localization-based tracking. In CVPR Workshop, 2021 [Google Scholar]
  15. Luna E, San Miguel JC, Ortego D, Martínez JM. Abandoned Object Detection in Video-Surveillance: Survey and Comparison. Sensors (Basel). 2018;18(12):4290. Published 2018 Dec 5. doi:10.3390/s18124290 [Google Scholar]
  16. V. Ghanavanth et al., ”Smart CCTV surveillance system for intrusion detection with live streaming”, 3rd IEEE International Conference RTEICT, 2018. [Google Scholar]
  17. Apoorva Raghunandan, Pakala Raghav and HV Ravish Aradhya,”Object detection algorithms for video surveillance applications”, ICCSP, 2018. [Google Scholar]
  18. Archana Kalyankar, Shikha Nema and Umesh Mahind,”Advance and automatic motion detection prediction data association with object tracking system”, International Conference ICIRCA, 2018. [Google Scholar]
  19. Suma, G. J., Lalitha, R. V. S. Vehicular Ad hoc Networks: A hybrid approach to data dissemination in exigency situations. Wireless Netw 22, 1725– 1737 2016. [Google Scholar]
  20. Lalitha RVS, Srinivas R, Kumar PSVVSR, Kavitha K, Sameera PVSNS. Intelligent signalling system to control traffic in vehicular ad hoc networks. Indian Journal of Science and Technology. 13(28). [Google Scholar]
  21. Lalitha, R. V. S., Kavitha, K., Krishna Rao, N. V., Rama Mounika, G., Sandhya, V., Smart surveillance with smart doorbell, IJITEE, Volume-8 Issue-8, June 2019, pp 1841-44. [Google Scholar]
  22. Nayak, Padmalaya, K. Kavitha, and Nausheed Khan.”Cluster head selection in wireless sensor network using bio-inspired algorithm.” In TENCON 2019-2019 IEEE Region 10 Conference (TENCON), pp. 1690-1696. IEEE, 2019. [Google Scholar]
  23. Reddy, T. Raghunadha, B. Vishnu Vardhan, and P. Vijaypal Reddy.”A survey on authorship profiling techniques.” International Journal of Applied Engineering Research 11, no. 5 (2016): 3092-3102. [Google Scholar]
  24. Kumar, Praveen, Ayush Singhal, Sanyam Mehta, and Ankush Mittal.”Real-time moving object detection algorithm on high-resolution videos using GPUs.” Journal of Real-Time Image Processing 11, no. 1 (2016): 93-109. [Google Scholar]
  25. Dhanalaxmi, B., G. Apparao Naidu, and K. Anuradha.”Adaptive PSO based association rule mining technique for software defect classification using ANN.” Procedia Computer Science 46 (2015): 432-442. [Google Scholar]
  26. Kumar, Singamaneni Kranthi, Pallela Dileep Kumar Reddy, Gajula Ramesh, and Venkata Rao Maddumala.”Image transformation technique using steganography methods using LWT technique.” Traitement du Signalvol 36 (2019): 233-237. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.