抄録
CH-004
Activity recognition based on egocentric object detection
◎Saptarshi Sinha・Hiroki Ohashi・Mitsuhiro Okada・Takuto Sato・Katsuyuki Nakamura(所属なし)
This paper studies first person activity recognition using egocentric videos. Object information is important for activity recognition but how much fine-grained object knowledge is helpful for the process is still an unresolved research area. We conducted an extensive study on how much fine-grained object location is useful for classifying activities. We experimentally found that too much fine or course grained information can harm activity recognition. We concatenated the object features from fine-tuned YOLOv3 to the features calculated by pretrained 3D-ResNext for activity recognition. We achieved a f-measure of 0.669, which is higher than when only 3D-ResNext features are used(0.653).