## 2 Literature Review  
### Behavioral Research Based on Computer Vision  
The combination of computer vision and behavioral imaging has changed the situation where previously, data was difficult to obtain and behavioral data was difficult to use efficiently, providing new possibilities for urban research. Scholars have introduced computer vision into urban research, and the basic method is to use cameras and drones to collect urban resident activity images, use computer vision to detect and obtain basic behavioral data, and then carry out research on urban-related issues. Currently, the types of basic data obtained based on computer vision and the research directions are as follows.  
Pedestrian count statistics are obtained to explore the use of space. For example, [PERSON] (2005) used a head detection algorithm to count the number of users entering a space and the number of users sitting at a fountain, etc., to evaluate the quality of architectural design from the perspective of use [18]. [PERSON] et al. (2020) detected the specific positions of people in pedestrian images, mapped them to real-world positions, and explained their use of space [19].  
Pedestrian spatial distribution data are obtained to explore spatial characteristics. [PERSON] et al. (2017) and [PERSON] (2018) used optical flow to extract pedestrian trajectories on urban pedestrian streets [16; 17]; [PERSON] and [PERSON] (2022) collected pedestrian video data from campuses and shopping malls and used multi-target tracking technology to extract pedestrian speed and spatial distribution differences; and [PERSON] et al. (2022) (2022) and [PERSON] [20] (2022) used multi-target tracking technology in videos to extract crowd movement trajectories to characterize spatial vitality.  
Pedestrian attribute data are obtained to explore spatial characteristics from the perspective of different populations. For example, [PERSON] et al. (2021) (2021) further subdivided pedestrians by age and gender, such as setting up three categories of young, adult, and elderly populations. Through four stages of pedestrian detection, feature extraction, identity tracking, and motion analysis, pedestrian attribute recognition and tracking are achieved, and refined pedestrian trajectory information is obtained.  
Pedestrian motion data are obtained to explore their influencing factors. For example, [PERSON] (2020) (2020) and others used computer vision technology to record pedestrian walking speed and explored how the weather and climate affect walking speed and to what extent. [PERSON] (2023) (2023) counted street pedestrian data and found that the walking speed in different sub-areas of the street was significantly different and proposed that walking speed is related to the category, brand, and decoration of shops.  
Overall, the use of computer vision for urban behavioral analysis is in its infancy. Behavioral data mostly consist of basic statistical parameters such as population count, pedestrian trajectory, and walking speed. They are limited to describing the spatiotemporal characteristics of single activities and mobile behaviors, simplifying the user's stay in space as their use of space, and ignoring the diversity of activities and the exploration of interactions with space.