Abstract
Self-service bag drop efficiently assists passengers to check-in their baggage in the airport. Nevertheless, the baggage appearance transportability cannot be accurately detected by existing self-service bag drop equipment. We plan to adopt a convolutional neural network with video input to detect the appearance transportability of baggage. However, public baggage picture datasets are captured in the daily background, thus existing approaches trained on these datasets achieve imprecise performance for airport self-service bag drop. We introduce a new dataset for airport self-service bag drop named ASS-BD and a novel sequential hierarchical sampling multi-object tracker. Most of the video clips that comply with the consignment regulations were recorded in the airport scene. Video clips that do not comply with the consignment regulations were recorded in the laboratory simulation scene. A sequential hierarchical sampling multi-object tracking baseline is adopted to solve some problematic frames due to part occlusion, rare pose, and motion blur. We conduct experiments to demonstrate that our dataset is suitable for the airport self-service bag drop scenario. Our approach is capable of the inspection task of air baggage appearance transportability in real-time.
Highlights
IntroductionA. BACKGROUND Airport Self-service bag drop can reduce the check-in time of passengers, improve the passenger experience, and maximize the throughput of terminal passengers, which is an essential means to simplify the check-in process
Where GT is the number of ground truth boxes, FN is the number of false negatives in the whole video, FP is the number of false positives in the whole video, and IDSW is the total number of ID switches
IDFN is the sum of weights from the selected false negative ID edges, and IDFP is the sum of weights from the selected false positive ID edges [29]
Summary
A. BACKGROUND Airport Self-service bag drop can reduce the check-in time of passengers, improve the passenger experience, and maximize the throughput of terminal passengers, which is an essential means to simplify the check-in process. Detecting the transportability of baggage is the key to self-service bag drop for determining whether the passenger’s bag meets the check-in conditions. With the rapid development of artificial intelligence and human-computer interaction, we have developed self-service bag drop equipment which is used to check whether the air baggage meets the check-in conditions and applied them to Beijing Daxing International Airport, Guangzhou Baiyun International Airport, and Tianjin Binhai International Airport. Self-service bag drop could detect whether the passenger’s baggage meets the check-in regulations from the International. The regulations of baggage transportability are about the weight, size, and ‘‘appearance transportability’’. In the following, ‘‘appearance transportability’’ will be used to represent meeting one of the above regulations
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