This thesis investigates the application of knowledge transfer techniques to process event-based data forperson detection in area surveillance. A teacher-student model setup is employed, where both modelsare pretrained on conventional visual data. The teacher model processes visual images to generate targetlabels for the student model trained on event-based data, forming the baseline model. Building onthis, the project incorporates feature-based knowledge transfer, specifically transferring features fromthe Feature Pyramid Network (FPN) component of the Faster R-CNN ResNet-50 FPN network. Resultsindicate that response-based knowledge transfer can effectively finetune models for event-based data.However, feature-based knowledge transfer yields mixed results, requiring more refined techniques forconsistent improvement. The study identifies limitations, including the need for a more diverse dataset,improved preprocessing methods, labeling techniques, and refined feature-based knowledge transfermethods. This research bridges the gap between conventional object detection methods and event-baseddata, enhancing the applicability of event cameras in surveillance applications.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-205562 |
| Date | January 2024 |
| Creators | Suihko, Gabriel |
| Publisher | Linköpings universitet, Institutionen för systemteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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