AI/ML Algorithms for Video Data Filtration
| dc.contributor.author | Amin, Siddharth | |
| dc.contributor.author | Atwine, Dean | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
| dc.contributor.examiner | Tsigas, Philippas | |
| dc.contributor.supervisor | Ali-Eldin Hassan, Ahmed | |
| dc.date.accessioned | 2023-12-15T15:31:09Z | |
| dc.date.available | 2023-12-15T15:31:09Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | Video cameras are ubiquitous in today’s society, with cities and organizations steadily increasing the size and scope of their deployments. These applications have benefited from cloud computing’s large-scale computing and storage capabilities over the last two decades. The massive amounts of data generated by these high-definition cameras are proving too large to transport and process in real-time in the cloud. Many critical applications, such as public safety, surveillance, and traffic control, rely heavily on video cameras. Filtering out frames that do not contain relevant information for the query at hand is a common (and natural) strategy used by systems to improve efficiency. However, this necessitates that the filtering algorithm can contextually decide on if the frame is relevant or not. This research looks into the creation of a video analytics pipeline that uses computer vision tasks, object classification models, and a prioritization algorithm to effectively filter frames from multiple cameras while dealing with the over subscription of streams on a processing node and sending only relevant frames for further processing. In this thesis, we examine multiple light-weight computer vision and classification models that can be used to classify if a frame has a contextually interesting object. We then design a pipeline where we use techniques such as frame-differencing, light-weight Deep-Neural Networks(DNNs), and a frame prioritization algorithm to decide on which frames would be processed in the case of overprovisioning and in what order. Our results show that our framework can accommodate up to 85% more streams than running with out the framework. | |
| dc.identifier.coursecode | DATX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/307433 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | Computer Vision | |
| dc.subject | Object Classification | |
| dc.subject | Video Filtration Pipeline | |
| dc.subject | Machine Learning | |
| dc.subject | Prioritization Scheduling | |
| dc.title | AI/ML Algorithms for Video Data Filtration | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Computer systems and networks (MPCSN), MSc |