Computer Vision: A Review of Detecting Objects in Videos – Challenges and Techniques
DOI:
https://doi.org/10.3991/ijoe.v18i01.27577Keywords:
Computer Vision, Traffic Safety Applications, Object Detection, AI models, Video Analysis, Convolutional Neural Network, Tensor-Flow, Lanes Viola-tion TrackingAbstract
Traffic safety aims to change the attitude of citizens towards careless traffic on the roads, making this the first step towards changing behavior. Also, teach the rules of safe pedestrian behavior and minimize the risks of road accidents. So many regulations have been set to avoid road accidents and traffic jams, which is the study scope of this paper using IT technology. With the expanding interests in Computer vision use cases such as vehicles self-driving, face recognition, intelligent transportation frameworks and so on individuals are hoping to assemble custom AI models to recognize and distinguish specific objects. Object detection is part of a computer's vision where objects that can be observed externally and are found in videos can be identified and tracked by computers. Therefore, object tracking is an important part of video analysis. There are many proposed methods such as Tracking, Learning, Detection, Mean shift and MIL. In this paper, the computer vision state in object detecting domain along with its challenges are discussed, also we address some requirements and techniques to overcome these challenges. Finally, TensorFlow technology is presented as a recommended solution to support Lane’s violation.
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Published
2022-01-26
How to Cite
Hammoudeh, M. A. A., Alsaykhan, M., Alsalameh, R., & Althwaibi, N. (2022). Computer Vision: A Review of Detecting Objects in Videos – Challenges and Techniques. International Journal of Online and Biomedical Engineering (iJOE), 18(01), pp. 15–27. https://doi.org/10.3991/ijoe.v18i01.27577
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Copyright (c) 2021 Mohammad Ali A. Hammoudeh, Mohammad Alsaykhan, Ryan Alsalameh, Nahs Althwaibi
This work is licensed under a Creative Commons Attribution 4.0 International License.