## 5 Conclusions  
In this paper, we introduced the position-information feature-image added two-stream CNN for pedestrian behavior recognition with multitask learning and a detection and tracking algorithm. In multitask learning, to increase the performance of action-attribute recognition, the two-stream CNN is used based on the binary descriptor dense SIFT flow, not the optical flow. Then, to increase the performance of cross-attribute recognition, the position-information feature image generated by a Siamese tracker is added to the two-stream CNN. By increasing the action and cross attributes, the proposed method has higher performance than state-of-the-art methods on the JAAD dataset. Moreover, the proposed method guarantees the high accuracy of pedestrian-action recognition in vehicle-moving urban environments by using the YOLO detection and Siamese tracker instead of the ground-truth boundary box. Thus, the proposed algorithm can be adopted for various applications in autonomous vehicles. In recent years, autonomous driving vehicles have begun to appear, and many studies have been conducted on pedestrian safety. Predicting pedestrian behavior has become an issue in saving pedestrian lives, but there is a problem because pedestrian behavior cannot be predicted with a dynamic model. It is necessary to understand and recognize the current pedestrian behaviors to predict future pedestrian behavior. In this paper, we show that the performance of pedestrian behavior recognition should be high with various attributes. Until now, the proposed method has limitation in that it can only recognize the walking and crossing road attributes not crossing intersection. Image can distinguish the pedestrian who crosses the road horizontally but does not cross the road vertically. Therefore, there is still room for improvement of recognizing the intersection crossing. It is necessary to study the vertical crossing attribute, then we have studied 3D detection by using point cloud which would be fused with proposed networks. The proposed method can facilitate saving pedestrians by adopting the advanced driver assistance system (ADAS)application. In addition, the proposed method can be adopted not only autonomous vehicle but also vehicle-to-infrastructure (V2I). For example, traffic sign pole alert warns the pedestrian who acts the strange behavior or sends the information of pedestrian behavior to the autonomous vehicle. This future technology can be applied at the urban planning policy or street design. We hope this research promotes further research on pedestrian behavior recognition for safety. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Ground-Truth Boundary Box** & **Cross Accuracy** \\ \hline [PERSON] [42] & O & 61.31\% \\ \hline [PERSON] [39] & O & 79.28\% \\ \hline [PERSON] [26] & O & 81.00\% \\ \hline [PERSON] [27] & O & 81.23\% \\ \hline [PERSON] [43] & O & 86.70\% \\ \hline [PERSON] [28] & O & 88.00\% \\ \hline [PERSON] [46] & O & 84.89\% \\ \hline Two-Stream CNN ([PERSON] [25]) & O & 76.44\% \\ \hline Proposed Method & O & 79.13\% \\ \hline Proposed Method & X & 72.90\% \\ with Detecting and Tracking algorithm & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparison of pedestrian cross-attribute recognition for state-of-the-art in single-task learning algorithm with the JAAD testing dataset. **Author Contributions:** Conceptualization, S.K.P. and M.T.L. ; Data curation, S.K.P. ; Formal analysis, S.K.P., D.S.P. and M.T.L. ; Methodology, S.K.P., J.H.C., M.T.L. and D.S.P. ; Software, S.K.P. and D.S.P. ; Validation, M.T.L. and D.S.P. ; Writing--original draft, S.K.P. and M.T.L. ; Writing--review and editing, M.T.L. and D.S.P. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korean government(MSIT) (No. 2022R1F1A1073543). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.