Centroid based pose ratio for pedestrian action recognition

This study explores a unique pose of human walking by proposing centroid based pose ratio for pedestrian action recognition. Our proposal uses width and height of bounding box, and also centroid of human shape to compute a pose ratio. Then, the proposed model incorporates the pose ratio with pedestrian lateral speed, walking direction and spatial layout of the environment to perform action classification. Lateral speed of pedestrian is determined by using point tracking on the projected point of centroid of detected human on the ground plane. Walking direction of object is estimated by using concatenated consecutive frames. Context of road is used to define the spatial layout. Pedestrian is detected in which location, road or non-road zone. Lane detection is performed for estimating the road boundary as the road zone. The proposed algorithm is evaluated on publicly available datasets. The performance results are presented.