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Vinotha, R.
- An Application of a Deep Learning Algorithm for Automatic Detection of Unexpected Accidents under Bad CCTV Monitoring Conditions In Tunnels
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1 Assistant Professor, Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 Assistant Professor, Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 7, No 1 (2021), Pagination: 20-24Abstract
Roads in tunnels vary in many ways from certain sections of open roads. For many drivers, the subway is an unusual driving area in a road network that can create even pressure. In conjunction with the Advanced Learning Network known as the Object Detection and Monitoring System, a quick and standard tracking system used for detecting automatically and unexpected incidents monitors on CV tunnels, Writing weight acquisition, (2) standings, (3) people outside the tunnel (4) fire. Odts accepts the video frames as input to obtain the location binding (BBox) result for object binding and compares BBoxes of current and previous video images to give each moving object a unique identification number. With this system, you can track a moving object over time, which is unfamiliar with the normal object detection settings. An in-depth learning model was developed in the ODTS with a series of graphically designed (AP) data sets of 0.8479, 0.7161, and 0.9085 values for direct, automatic, human, and product items, respectively. Subsequently, based on the in-depth learning model developed, the ODTS video risk assessment system was evaluated using four risk video recordings for each risk. This allows the system to detect all crashes in 10 sec. The ODTS acquisition capabilities can be automatically upgraded without changes to program codes if the training database is improved.Keywords
Object Detection R-CNN, Object Tracking, Object Detection and Tracking system, Detection for Unexpected Events, Tunnel CCTV Accident Detection System
References
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- L. Cao, Q. Jiang, M. Cheng, C. Wang, “Robust vehicle detection by combining deep features with exemplar classification”, Neuro Computing, 2016, vol. 215, pp. 225-231.
- A. Arinaldi, J. A. Pradana, A. A. Gurusinga, “Detection and classification of vehicles for traffic video analytics”, Procedia computer science, 2018, vol. 144, pp. 259-268.
- K. B. Lee, H. S. Shin, D. G. Kim, “Development of a deep-learning based automatic tunnel incident detection system on cctvs”, in Proc. Fourth International Symposium on Computational Geomechanics, 2018, pp. 140-141.
- S. Ren, K. He, R. Girshick, J. Sun, “Faster R-CNN: Towards RealTime Object Detection with Region Proposal Networks”, in Proc. Neural Information Processing Systems, 2015, pp. 91-99.
- A. Bewley, Z. Zongyuan, L. Ott, F. Ramos, B. Upcroft, “Simple Online and Realtime Tracking”, in Proc. IEEE International Conference on Image Processing, 2016, pp. 3464-3468.
- K. B. Lee, H. S. Shin, D. G. Kim, “Development of a deep-learning based automatic tracking of moving vehicles and incident detection processes on tunnels”, Korean Tunnelling and Underground Space Association, 2018, vol. 20, no.6, pp. 1161-1175.
- Architectural Structures in Convolutional Neural Network for Person Re-Identification
Abstract Views :122 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Department of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 Assistant Professor, Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
1 Assistant Professor, Department of Information Technology, M.Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
2 Assistant Professor, Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 7, No 1 (2021), Pagination: 25-30Abstract
Convolutional network in deep learning algorithm has many architectural structures for the person re-identification to increase the network accuracy. Observation of the same person can be matched in different occasions like time, cameras is the Person recognition on appearance based classification. Many years researchers on computer vision realized Person re-identification has been tricky problem which video includes frame taken at different place, pose, condition, lighting condition, camera, occlusions, background and appearance. Here we implement different architectural structure in convolutional network in our dataset to give different accuracy and different error rate to analyses the best structural to recognize the person in different cameras. Tracking and finding a person deals with the security issue where many places like airports, streets, colleges, shopping malls, theaters and many other public places for identification of fraud cases.Keywords
Convolutional Neural Network, Deep Learning, Architectural Structure (LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, ResNext, DenseNet),, Person Recognition.References
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- Detection of Astonishing Accidents in Tunnel using Deep Learning Algorithm
Abstract Views :218 |
PDF Views:0
Authors
Affiliations
1 Assistant Professor, Department of Information Technology, M.Kumarasamy College of Engineering, Karur,Tamil Nadu, IN
1 Assistant Professor, Department of Information Technology, M.Kumarasamy College of Engineering, Karur,Tamil Nadu, IN
Source
International Journal of Emerging Trends in Science & Technology, Vol 7, No 2 (2021), Pagination: 22-26Abstract
Roads in tunnels vary in many ways from certain sections of open roads. For many drivers, the subway is an unusual driving area in a road network that can create even pressure. In conjunction with the Advanced Learning Network known as the Object Detection and Monitoring System, a quick and standard tracking system used for detecting automatically and unexpected incidents monitors on CV tunnels, Writing weight acquisition, (2) standings, (3) people outside the tunnel (4) fire. Odts accepts the video frames as input to obtain the location binding (BBox) result for object binding and compares BBoxes of current and previous video images to give each moving object a unique identification number. With this system, you can track a moving object over time, which is unfamiliar with the normal object detection settings. An in-depth learning model was developed in the ODTS with a series of graphically designed (AP) data sets of 0.8479, 0.7161, and 0.9085 values for direct, automatic, human, and product items, respectively. Subsequently, based on the in-depth learning model developed, the ODTS video risk assessment system was evaluated using four risk video recordings for each risk. This allows the system to detect all crashes in 10 sec. The ODTS acquisition capabilities can be automatically upgraded without changes to program codes if the training database is improved.Keywords
Object Detection R-CNN, Object Tracking, Object Detection and Tracking System, Detection for Unexpected Events, Tunnel CCTV Accident Detection SystemReferences
- L. Cao, Q. Jiang, M. Cheng, C. Wang, “Robust vehicle detection by combining deep features with exemplar classification,” Neurocomputing, 2016, vol. 215, pp. 225-231.
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- K. B. Lee, H. S. Shin, D. G. Kim, “Development of a deep-learning based automatic tunnel incident detection system on cctvs,” in Proc. Fourth International Symposium on Computational Geomechanics, 2018, pp. 140-141.
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- A. Bewley, Z. Zongyuan, L. Ott, F. Ramos, B. Upcroft, “Simple Online and Realtime Tracking,” in Proc. IEEE International Conference on Image Processing, 2016, pp. 3464-3468.
- K. B. Lee, H. S. Shin, D. G. Kim, “Development of a deep-learning based automatic tracking of moving vehicles and incident detection processes on tunnels,” Korean Tunnelling and Underground Space Association, 2018, vol. 20, no.6, pp. 1161-1175.
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