# Human Behavior Analysis: A Survey on Action Recognition
## 4 Evaluation Protocols and Quantitative Analysis for Action Recognition  
In this section, we provide a performance comparison in Table 2 over a comprehensive list of 18 must-know methods in each category addressed in this survey, which each method was explained in Sections 2.1 and 2.2. The results are reported on six challengingbenchmarks, being the most popular datasets for evaluation comparison among each category. Likewise, the performance measures reported are the most typical ones for each category approach. The accuracies are directly reported from the original works.  
The evaluation protocol for local representation approaches is frame-level recognition, reporting the Top-1 accuracy as the performance measure (the average accuracy regarding the Top-1 class predicted by the model). For the global representation approaches, the evaluation protocol performed for action recognition is pixel-wise, adopting as the performance measure the mean average precision (mAP), which approximates the area under the precision-recall curve for each individual action class. Additionally, we also indicate the year of the method regarding when it was published.  
Aside from the intra-representations performance evolution, we can observe a significant difference between performances of local and global representations regarding RGB-based datasets (UCF-101 [95], HMDB-51 [96], UCF-101 24 [95] and J-HMDB-21 [101]). This is justified by the difficulty of the problem being solved. As described in Section 1 local representations are performing action recognition at the frame level, while global representations are performing at the pixel level, which becomes far more challenging. Despite skeleton-based methods working at the pixel level and achieving great performance on NTU RGB-D [102], this dataset was obtained from highly restricted settings. When applied to a more wild and challenging dataset, such as Kinetics-Skeleton [97], a notable drop in performance is observed, which indicates an important limitation of skeleton-based methods.  
\begin{table}
\begin{tabular}{l l l c c} \hline \hline  & **Method** & **Year** & **Top-1** & **Top-1** \\ \hline \multirow{4}{*}{**Voxel**} & & & _UCF-101_ & _HMDB-51_ \\  & Slow Fusion [19] & 2014 & 0.654 & - \\  & C3D [11] & 2015 & 0.823 \({}^{\ddagger}\) & - \\  & TS-LSTM [104] & 2019 & 0.943 & 0.690 \\  & H Two-stream I3D [31] & 2018 & 0.971 & 0.787 \\  & R(2+1)D [12] & 2018 & 0.973 \({}^{\ddagger}\) & 0.787 \({}^{\ddagger}\) \\  & HTNet [30] & 2019 & 0.978 & 0.765 \\  & Two-stream I3D [10] & 2017 & 0.979 & 0.802 \\  & R(2+1)D - BERT [37] & 2020 & 0.987 & 0.851 \\ \hline \hline \multirow{4}{*}{**Voxel**} & **Method** & **Year** & **mAP\({}_{\text{50}}\)** & **mAP\({}_{\text{50}}\)** \\ \cline{2-5}  & STEP [58] & 2019 & 0.750 & - \\ \cline{1-1}  & Faster R-CNN + I3D [54] & 2018 & 0.763 & 0.733 \\ \cline{1-1}  & MOC [59] & 2020 & 0.780 \({}^{\ddagger}\) & 0.708 \({}^{\ddagger}\) \\ \cline{1-1}  & VideoCapsuleNet [94] & 2018 & 0.786 \({}^{\ddagger}\) & 0.646 \({}^{\ddagger}\) \\ \cline{1-1}  & YOWO [55] & 2019 & 0.804 \({}^{\ddagger}\) & 0.757 \({}^{\ddagger}\) \\ \cline{1-1} \cline{2-5}  & ST-LSTM [69] & 2016 & 0.755 & - \\ \cline{1-1}  & ST-GCN [86] & 2018 & 0.883 \({}^{\ddagger}\) & 0.307 \({}^{\ddagger}\) \\ \cline{1-1}  & GCN-NAS [91] & 2020 & 0.957 & 0.371 \\ \cline{1-1}  & DGNN [87] & 2019 & 0.961 & 0.369 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Reproduced applying the original’s author configuration to confirm the results reported. \({}^{\ddagger}\)-\({}^{\ddagger}\) Result is not reported.  
\end{table}
Table 2: Performance summary of some reference action recognition methods from both categories, local and global representation approaches over their respective benchmarks in terms of accuracy and mean average precision.