level. (4) Boundary Sensitive Network (BSN) [@Lin:ECCV18] locates temporal boundaries with three actionness curves and reranks proposals with neural networks. (5) Single Stream Temporal action proposal (SST) [@Buch:CVPR17] builds a RNN-based action proposal network, which could be implemented in a single stream over long video sequences to produce action proposals. (6) Complementary Temporal Action Proposal (CTAP) [@Gao:ECCV18] balances the advantages and disadvantages between sliding window and actionness grouping approaches for final action proposal.

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[[|c|c|c@[ ]{}c|c|]{}]{} & & &\
& AR &   AR    & AUC & AUC\
& 37.9 & - & -& -\
& 50.1 & 73.2 & 65.7 & -\
& 53.2 & 74.2 & 66.2 & 66.3\
& **54.3** & **74.8** & **67.1** & **67.4**\

\[table4:1\]

![image](gaussian_case_camera){width="91.00000%"}

We adopt the standard metric of Average Recall in different IoU (AR) for action proposal on both datasets. Moreover, following the official evaluations in ActivityNet, we plot both Recall-IoU curve and Average Recall vs. Average Number of proposals per video (AR-AN) curve in Figure \[fig4:1\]. In addition to AR metric, the area under AR-AN curve (AUC) is also reported in Table \[table4:1\] as AUC is the measure on test server of ActivityNet. Overall, the performances across different metrics and two 