Monitoring systems, and computer implemented methods for processing data in monitoring systems, programmed to enable identification and tracking of human targets in crowded environments

Described herein is a method and computer system programmed to enable prediction of human activity. In one aspect, a method includes a capture phase (101), which includes capture of a stream of raw depth images from a depth camera device. A scene processing phase (102) is applied thereby to process the raw depth images from phase (101) to produce an upright pointcloud of foreground only points, aligned with an identified upright orientation (optionally via a ground plane). This is followed by a pointcloud segmentation stage (103) to spatially segment the upright pointcloud into smaller person proposal clusters which describe potential human targets. A pose extraction phase (104) then extracts a shoulder pose from each proposed cluster, rejecting proposals which violate some basic size and shape constraints. Valid poses are then passed for processing in a tracking phase (105). This includes tracking all detected person poses using a probabilistic estimator, such as a particle filter with social constraints to improve tracking robustness in densely crowded environments. Then, via an output phase (106), a final output is provided in the form of a stream of filtered poses with persistent tracking IDs which describe the trajectory of each person observed by the sensor.