Advances in Visual Perception for Dynamic Environment Monitoring
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| Formato: | Recurso digital |
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Zenodo
2020
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| _version_ | 1866901960493367296 |
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| author | Dr. Rukmini Devi Dr. Abhijit Kumar |
| author_facet | Dr. Rukmini Devi Dr. Abhijit Kumar |
| contents | <p>Video surveillance is an active research topic in computer vision that tries to detect, recognize and track objects over a sequence of images and it also makes an attempt to understand and describe object behavior by replacing the aging old traditional method of monitoring cameras by human operators. Object detection and tracking are important and challenging tasks in many computer vision applications such as surveillance, vehicle navigation and autonomous robot navigation. Object detection involves locating objects in the frame of a video sequence. Every tracking method requires an object detection mechanism either in every frame or when the object first appears in the video. Object tracking is the process of locating an object or multiple objects over time using a camera. The high powered computers, the availability of high quality and inexpensive video cameras and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms. There are three key steps in video analysis, detection interesting moving objects, tracking of such objects from each and every frame to frame, and analysis of object tracks to recognize their behavior. The main reason is that they need strong requirements to achieve satisfactory working conditions, specialized and expensive hardware, complex installations and setup procedures, and supervision of qualified workers. Some works have focused on developing automatic detection and Tracking algorithms that minimizes the necessity of supervision. They typically use a moving object function that evaluates each hypothetical object configuration with the set of available detections without explicitly computing their data association.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19308151 |
| institution | Zenodo |
| language | |
| publishDate | 2020 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Advances in Visual Perception for Dynamic Environment Monitoring Dr. Rukmini Devi Dr. Abhijit Kumar KF – Kalman Filter OF- Optical Flow GMM- Gaussian Mixture Model <p>Video surveillance is an active research topic in computer vision that tries to detect, recognize and track objects over a sequence of images and it also makes an attempt to understand and describe object behavior by replacing the aging old traditional method of monitoring cameras by human operators. Object detection and tracking are important and challenging tasks in many computer vision applications such as surveillance, vehicle navigation and autonomous robot navigation. Object detection involves locating objects in the frame of a video sequence. Every tracking method requires an object detection mechanism either in every frame or when the object first appears in the video. Object tracking is the process of locating an object or multiple objects over time using a camera. The high powered computers, the availability of high quality and inexpensive video cameras and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms. There are three key steps in video analysis, detection interesting moving objects, tracking of such objects from each and every frame to frame, and analysis of object tracks to recognize their behavior. The main reason is that they need strong requirements to achieve satisfactory working conditions, specialized and expensive hardware, complex installations and setup procedures, and supervision of qualified workers. Some works have focused on developing automatic detection and Tracking algorithms that minimizes the necessity of supervision. They typically use a moving object function that evaluates each hypothetical object configuration with the set of available detections without explicitly computing their data association.</p> |
| title | Advances in Visual Perception for Dynamic Environment Monitoring |
| topic | KF – Kalman Filter OF- Optical Flow GMM- Gaussian Mixture Model |
| url | https://doi.org/10.5281/zenodo.19308151 |