  Detection of illegal and threatening items in baggage is one of the utmost
security concern nowadays. Even for experienced security personnel, manual
detection is a time-consuming and stressful task. Many academics have created
automated frameworks for detecting suspicious and contraband data from X-ray
scans of luggage. However, to our knowledge, no framework exists that utilizes
temporal baggage X-ray imagery to effectively screen highly concealed and
occluded objects which are barely visible even to the naked eye. To address
this, we present a novel temporal fusion driven multi-scale residual fashioned
encoder-decoder that takes series of consecutive scans as input and fuses them
to generate distinct feature representations of the suspicious and
non-suspicious baggage content, leading towards a more accurate extraction of
the contraband data. The proposed methodology has been thoroughly tested using
the publicly accessible GDXray dataset, which is the only dataset containing
temporally linked grayscale X-ray scans showcasing extremely concealed
contraband data. The proposed framework outperforms its competitors on the
GDXray dataset on various metrics.
