# An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos +
## 4 Experiments and Results
### Experimental Results
Figure 6: The accuracy comparison between GRU and LSTM with different pooling methods: (**a**) MAP comparison; (**b**) Top-1 accuracy comparison. We compared the search accuracy between our method and the RCNN method with different numbers of video clips. For the same comparison, the RCNN method also uses GRU to generate spatio-temporal features. As the number of video clips increases from 6K to 15K, the MAP value of our method decreases from 0.795 to 0.662 in Figure 6(a), and the top-1 accuracy of our method decreases from 0.934 to 0.897 in Figure 6(b). This is because more video clips generate more spatio-temporal features which are affected by complex backgrounds, illuminations, viewpoints, occlusions, and various appearances of persons. Due to dimension reduction in our method and the increasing number of spatio-temporal features, the distinction between different spatio-temporal features may be decreased to some extent, which reduces the search accuracy. Although the search accuracy of our method is decreased, it is still better than that of the RCNN method, which proves that the spatio-temporal features generated by our method are more representative and ensure search accuracy. We also compared the search accuracy of our method with the PPPS method with different numbers of video clips. In Figure 8, as the number of video clips increases from 6K to 15K, the search accuracy of our method and the PPPS method decreases, but the accuracy of our method is better than that of the PPPS method. Unlike our method, the PPPS method uses a different way to organize the temporal relationship of video frames. In the PPPS method, the IDE feature is used to represent the person in each video frame, and then it is used to explore visual and temporal links of person identities and compute the similarity. Compared with the IDE feature representing a person in a single frame, the spatio-temporal features in our method represent persons between adjacent frames, which can better represent persons in the surveillance videos and increase the discrimination between different persons. Therefore, our method achieves greater search accuracy than the PPPS method. To evaluate search efficiency, the average search time was used to compare our method with the RCNN method and the PPPS method. In Figure 9, as the number of video clips increases from 6 K to 15 K, all the search times of the three methods increase because more video clips increase the computational cost of the search process. However, the search time of our method is lower than that of the RCNN method and the PPPS method. Compared with the RCNN method using the pedestrian proposal net, our method generates fewer spatial features by utilizing the Yolo network in the stage of person detection, thereby reducing the computational cost of the subsequent process and obtaining a shorter search time. Different from our method and the RCNN method, the PPPS method explores the  
Figure 7: The accuracy comparison between our method and the RCNN method with different numbers of video clips: (**a**) MAP comparison; (**b**) Top-1 accuracy comparison. visual and temporal links to search for a target person, thus it has to calculate the similarity of these links for each frame, which greatly increases the computational cost of the search process. In contrast, our method employs LSH to organize the spatio-temporal features and compute the similarity, thereby reducing the computational cost. Compared with the RCNN method and the PPPS method, our method achieves the shortest search time and improves search efficiency.