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Magnitude-Orientation Stream Network and Depth Information applied to Activity Recognition

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Academic year: 2021

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Figure

Figure 1: Architectures considered in this work for extracting spatiotemporal information.
Figure 2: Comparison between optical flow displacement information, magnitude and orien- orien-tation extracted from two consecutive frames (t and t + 1) of an activity sample extracted from the UCF101 dataset [21].
Figure 2 illustrates the original magnitude information and the weighted magnitude information by depth
Figure 4: Confusion matrices on UCF101 split 1. False positives and false negatives were highlighted to show where each method fails.
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