Designing robust activity detectors for fixed camera surveillance video requires knowledge of the 3-D scene. This paper presents an automatic camera calibration process that provides a mechanism to reason about the spatial proximity between objects at different times. It combines a CNN-based camera pose estimator with a vertical scale provided by pedestrian observations to establish the 4-D scene geometry. Unlike some previous methods, the people do not need to be tracked nor do the head and feet need to be explicitly detected. It is robust to individual height variations and camera parameter estimation errors.
@cite_10: This work presents a vision-based system for detecting and localizing pedestrians in road environments by means of a statistical technique. Initially, attentive vision techniques relying on the search for specific characteristics of pedestrians such as vertical symmetry and strong presence of edges, allow to select interesting regions likely to contain pedestrians. These regions are then used to estimate the localization of pedestrians using a Kalman filter estimator.
Limited work has also been done for 3-D location estimation for pedestrians from 2-D images. @cite_10 present a method that relies on two cameras for stereo refinement of bounding boxes before estimation.