What are the limitations of location-based analysis methods (WiFi, mobile signaling, GPS) for studying human behavior, and how do computer vision methods address these limitations?

Location-based methods like WiFi, mobile signaling, and GPS can track spatiotemporal trajectories at large scales with minimal interference, but they only capture location information. They cannot provide deeper insights into micro-level behavioral details, such as gait or body posture, leaving fine-grained behavioral studies difficult to quantify. Computer vision methods address this by adding object detection, tracking, and pose estimation to extract detailed key-point data and enable automated, visual, real-time analysis of micro-behaviors.

The source explains that location-based analysis methods, including WiFi, mobile signaling, and GPS, track individuals' spatiotemporal trajectories. These methods expand the scale of studies on movement patterns while minimizing interference with subjects. However, they are limited to capturing location information only, which improves the breadth and efficiency of data acquisition but does not provide deeper insights into micro-level behavioral information. Consequently, fine-grained behavioral studies still face challenges in quantitative representation. Computer vision methods, as described in the source, enable automated processing of video recordings. Object detection and tracking algorithms can identify pedestrians and perform counting tasks in a large-scale, contactless, and automated way. Unlike location-based methods, video-based approaches offer a more visualized computation process, fast response times, and real-time monitoring of pedestrian flow. More importantly, by integrating human pose estimation algorithms, computer vision can obtain three-dimensional coordinates of human key points, significantly enhancing the granularity of behavioral data collection and enabling computation of meaningful micro-behavior metrics, such as gait analysis and social distancing indicators.