# Video-Based Recognition of Human Activity Using Novel Feature Extraction Techniques
## 4 Results  
We start by reporting the classification accuracies of the proposed solutions. As practiced in the literature, we report the average classification accuracy of the three data splits of the JHMDB dataset. We also compare our results against similar solutions reported in the literature. Our comparison against the existing literature is based on what is summarized in the recent work [10] which presented recent and outstanding results using an innovative solution, as summarized in Section 1. In the proposed solution, the number of trees used in the random forests classifier was 180, which was set empirically. The length of the feature vector of the HEVC solution of Section 2.1 was 67x2, pertaining to the mean and standard deviation of the HEVC features. In contrast, the length of the feature vector of the projected motion vector phases in Section 2.2 was equal to the number of video frames in a sequence (minus one, as motion estimation starts from the second frame). In the HEVC video coding, we used typical coding parameters such as a maximum coding unit size of 64 \(\times\) 64 pixels and a motion estimation range of 32 pixels. The quantization scale was set to 22, which is neither coarse nor fine. As presented in Table 1, in comparison with the reviewed work, the proposed solution, which is based on projecting motion vector phases, scored with an average accuracy of 68.8%. This can be justified by the fact that each video frame in this solution was projected into one scalar point, as elaborated upon in Section 2.2. On the other hand, the solution based on feature variables from HEVC encoding ranked the second with an average accuracy of 74.2%. The latter solution achieved higher classification accuracy, as motion vectors are an integral part of the HEVC features in addition to many other coding features, as listed in Section 2.1. In deep learning, typically, multi-solutions can be fused to generate more accurate results. Fusion can be achieved on score level or on a feature level. The former is irrelevant to RFs, as classification scores are not generated, and the fact that only two solutions are proposed means that the solutions cannot be fused on a class label level, as ties cannot be resolved with an even number of classifiers. This leaves the option of feature fusion, where the features of the projected motion vector phases and HEVC features are concatenated. Having tested that, we noticed that the classification accuracy did not improve. This is justified by the fact that HEVC features already include histograms of motion vectors generated from the motion estimation process of the coder. The fact that the second proposed solution is mainly based on motion vectors necessitates that fusing both features does not add class discriminating information and thus does not enhance the classification accuracy. \begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Accuracy** \\ \hline PoTion [20] & 57.0\% \\ P-CNN [21] & 61.1\% \\ Action Tubes [22] & 62.5\% \\ EleAtt-GRU [23] & 62.9\% \\ Zero Padding (with features from VGG-Net) [14] & 68.8\% \\
**Proposed projection of motion vector phases** & **68.8\%** \\ PA3D [24] & 69.5\% \\ Temporal Pyramid (with features from VGG-Net) [14] & 69.8\% \\ MR Two-Stream R-CNN [25] & 71.1\% \\ DR2N [26] & 71.8\% \\ Zero Padding (with features from GoogleNet) [14] & 72.3\% \\ Generalized Rank Pooling with the improved Trajectory Features [27] & 73.7\% \\
**Proposed feature variables based on HEVC coding** & **74.2\%** \\ Temporal Pyramid (with features from GoogleNet) [14] & 74.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of average classification accuracy of the proposed solutions against state-of-the-art solutions using the JHMDB dataset. Confusion matrices pertaining to both proposed solutions are displayed in Figure 5. The proposed solution using HEVC feature variables works well for some videos but not for others.