# A Distributed Automatic Video Annotation Platform
## 5 Evaluation and Analysis
### Experimental Analysis
(**c**) time (in seconds) required for the similarity measure APIs. Furthermore, we also computed the throughput of our proposed APIs, which is demonstrated in Figure 12a,b. This experiment shows that the throughput of our proposed APIs is increased if the number of nodes increases. From Figure 12a we can observe that, for temporal feature extraction, the proposed VLDTP-TOP can process 154 frames per second in one node and process 408 frames per second when three nodes are used. MBP can process the maximum number of frames among all the dynamic descriptors. Moreover, Figure 12 shows that our descriptor can achieve more than real-time performance with the current platform. Figure 12b shows that the proposed CLDTP can process 573 frames in one second and perform faster than real-time capture when three nodes are applied. However, LBP can process the maximum number of frames among all the spatial descriptors due to computational simplicity. Figure 13 demonstrates the precision for each category of the proposed color local directional ternary pattern (CLDTP) and VGGNet on STAD datasets. Though the AP of VGGNet shows better than our proposed CLDTP, CLDTP still works better for roads, fences, and tornadoes. Furthermore, Figure 14 illustrates that CLDTP is faster than vggNet for feature extraction. Figure 12: (**a**) Throughput (in seconds) of the temporal feature extraction APIs on STAD dataset and (**b**) throughput (in seconds) of the spatial feature extraction APIs on the STAD dataset. Figure 13: Precision of the CLDTP and VGGNet on STAD. Figure 15 represents the average precision (AP) of our proposed spatial feature extraction APIs. The comparison shows that our proposed descriptor provides better performance than the existing handcrafted algorithms: LBP, LTP, CLBP, LBPC [48], and DFT [8]. The existing descriptors only capture either the textural features or color features and ignore the shape information. In this work, we extracted color information, including the shape and texture information; therefore, our proposed CLDTP provides better performance than the existing descriptors. [PERSON] et al. [46] presented that accuracy will be improved if three prominent features, e.g., color, texture, and shape features, are preserved. However, CLDTP shows lower precision than the deep network VGGNet in some categories. Figure 16 illustrates the precision of each category for the proposed temporal feature descriptor VLDTP-TOP and the existing dynamic descriptors MBP and LBP-TOP on the STAD dataset. Proposed VLDTP-TOP performs better in almost every category. However, VLDTP-TOP cannot differentiate between smoke in explosions and tornados properly. Figure 17 demonstrates the average precision for the proposed temporal feature extraction APIs. LBP-TOP suffers from noise and is sensitive to illumination as it uses LBP for its computation and our proposed LDTP-TOP removes noises using mean deviation and 2 nd derivative Gaussian filter in the center pixel. Furthermore, we capture the gradient information with the dynamic texture, which leads to higher accuracy than LBP-TOP. Moreover, both LBP-TOP and LDTP-TOP use only 18 pixels out of  
Figure 14: Time required for feature extraction using CLDTP and VGGNet on STAD. Figure 15: Average precision (AP) of spatial feature extraction APIs on the STAD dataset. 27 pixels and use redundant information for some pixels. Therefore, it loses some crucial information, which leads to lower performance. To solve this problem, VLDTP-TOP considers volume information; it takes similar frame information for its computation to that used to discover orthogonal planes. We keep only the maximal information from both upper and lower patterns since max-pooling does not loose information, but rather, it preserves important features. Therefore, VLDTP-TOP outperforms the existing dynamic descriptors. In order to validate the performance of the proposed VLDTP-TOP on larger dynamic dataset, an experiment on UCF50 [52] was performed.