# Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
## 4 Experiments
### Comparison with State-of-the-Art Methods  
We compare the performance of our action detection method with state-of-the-art methods on THUMOS'14, including SCNN, CDC, SS-TAD, and so on. We group these methods according to the aforementioned detection frameworks. In addition to the results at various tIoU thresholds in Table 4, for each method, we also specify the fields of input and the involved features. The results show that our method achieves detection performance that is comparable to that of state-of-the-art methods. Some detection results of two testing videos are presented in Figure 8.  
We also compare our method with LearSubmission, SCNN, SS-TAD, and R-C3D for each category in THUMOS'14 at 0.5 tIoU. The Average Precision (AP) for each category is shown in Figure 9. It is noticed that our method performs best for two out of 20 action categories. Both of SS-TAD and R-C3D perform best for seven of 20 action categories. But, our method has the best overall performance among these methods, even though it only outperforms R-C3D and SS-TAD with a small margin. These results indicate that our method has a relatively balanced performance for different action categories. \begin{table}
\begin{tabular}{c c} \hline
**Input Fields** & **mAP (\%)** \\ \hline RGB & 25.2 \\ OF & 24.9 \\ RGB + OF & 29.6 \\ \hline \end{tabular}
\end{table} 
Table 3: Action detection results with 0.5 tIoU on THUMOS’14 using different input fields. Figure 7: Comparison of our DTSS-based proposal with the sliding window (SW)-based and temporal selective search (TSS)-based proposals. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**0.5**} & \multirow{2}{*}{**0.4**} & \multirow{2}{*}{**0.3**} & \multirow{2}{*}{**0.2**} & \multirow{2}{*}{**0.1**} & \multicolumn{2}{c}{**Input**} & \multirow{2}{*}{**Involved Features**} \\ \hline \multicolumn{6}{c}{**Classification then Post-Processing**} \\ \hline UnifiSubmission [42] & 0.9 & 1.4 & 2.1 & 3.4 & 4.6 & RGB + OF & iDT \\ PSDF [1] & 18.8 & 26.1 & 33.6 & 42.6 & 51.4 & RGB + OF & iDT \\ CUHKSubmission [22] & 8.3 & 11.7 & 14.0 & 17.0 & 18.2 & RGB + OF & iDT + CNN \\ \hline \multicolumn{6}{c}{**Temporal Upsampling**} \\ \hline CDC [13] & 23.3 & 29.4 & 40.1 & - & - & RGB & 3D CNN \\ TPC [14] & 28.2 & 37.1 & 44.1 & - & - & RGB & 3D CNN \\ \hline \multicolumn{6}{c}{**Single Stream**} \\ \hline SSAD [8] & 24.6 & 35.0 & 43.0 & 47.8 & 50.1 & RGB + OF & CNN + 3D CNN \\ SS-TAD [9] & 29.2 & - & - & - & - & RGB & 3D CNN \\ \hline \multicolumn{6}{c}{**Proposal then Classification**} \\ \hline LearSubmission [43] & 14.4 & 20.8 & 27.0 & 33.6 & 33.6 & RGB & SIFT [44] + Color + CNN \\ SparseLearning [45] & 13.5 & 18.2 & 25.7 & 32.9 & 36.1 & RGB & STIP [46] \\ SCNN [3] & 19 & 28.7 & 36.3 & 43.5 & 47.7 & RGB & CNN \\ Daps [26] & 13.9 & - & - & - & - & RGB & 3D CNN \\ SST [27] & 23 & - & - & - & - & RGB & 3D CNN \\ SelfAdaptive [47] & 27.7 & - & - & - & - & RGB & 3D CNN \\ R-C3D [25] & 28.9 & 35.6 & 44.8 & 51.5 & 54.5 & RGB & 3D CNN \\ Two-stream RNN [48] & 18.8 & 28.9 & 36.9 & 42.9 & 46.1 & RGB + OF & CNN \\ Multi-task Learning [4] & 19 & 28.9 & 36.2 & 43.6 & 47.7 & RGB & 3D CNN \\ TURN [18] & 25.6 & 34.9 & 44.1 & 50.9 & 54 & GRB + OF & CNN \\ Cascade [49] & 31 & 41.3 & 50.1 & 56.7 & 60.1 & RGB + OF & CNN \\ WeakSupervision [50] & 13.7 & 21.1 & 28.2 & 37.7 & 44.4 & RGB + OF & CNN \\ Generalization [51] & 28.2 & 39.8 & 48.7 & 57.7 & 64.1 & RGB & CNN \\
**Our method** & 29.6 & 40.1 & 48.9 & 57.3 & 64.7 & RGB + OF & 3D CNN \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison with state-of-the-art methods on THUMOS’14: mAP (%) with various tIoU thresholds (0.5, 04, 03, 02, 0.1) on THUMOS’14. Figure 8: Detection results of the testing videos video_test_0001038 and video_test_0000324 in THUMOS’14. Additionally, we perform the evaluation on ActivityNet1.3 dataset, whcih defines a 5-level hierarchy of action categories. Nodes on higher level represent more abstract action categories.