Baggage Weight Classification by Extracting Temporal Features of Body Sway Acquired from Depth Image Sequences

We propose a method for classifying the weight of baggage carried by a person in an upright posture by finding temporal cues of body sway from depth image sequences. When a standing person is viewed from an overhead depth camera, body sway, which is a slight movement that naturally occurred in the human body, is observed. We consider body sway as discriminative cues for baggage weight classification because it varies depending on the weight of baggage carried by a standing person. To find the cues of body sway from depth image sequences, we can use the existing feature extraction1). However, the accuracy of baggage weight classification is reduced if the existing feature extraction is simply performed. The existing feature extraction causes this problem by seeing both the motion and the shape representing each person's cue to identify people. We consider that the shape of a person does not change even if the weight of the baggage changes. To this end, we design a novel feature extraction that suppresses spatial cues of the shape of a person and emphasizes temporal cues of the motion using the head region's center position. The experimental results show that our feature extraction improves baggage weight classification accuracy compared to the existing feature extraction.