TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains

• URL: http://arxiv.org/abs/2311.03124v1
• Date: Mon, 6 Nov 2023 14:19:05 GMT
• Title: TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains
• Authors: Alexander Naumann, Felix Hertlein, Laura D\"orr, Kai Furmans
• Abstract summary: In this work, we focus on the use-case last-mile delivery, where only a single RGB image is taken and compared. We propose a tampering detection pipeline that utilizes keypoint detection to identify the eight corner points of a parcel. Experiments with multiple classical and deep learning-based change detection approaches are performed.
• Score: 45.62331048595689
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Due to the steadily rising amount of valuable goods in supply chains, tampering detection for parcels is becoming increasingly important. In this work, we focus on the use-case last-mile delivery, where only a single RGB image is taken and compared against a reference from an existing database to detect potential appearance changes that indicate tampering. We propose a tampering detection pipeline that utilizes keypoint detection to identify the eight corner points of a parcel. This permits applying a perspective transformation to create normalized fronto-parallel views for each visible parcel side surface. These viewpoint-invariant parcel side surface representations facilitate the identification of signs of tampering on parcels within the supply chain, since they reduce the problem to parcel side surface matching with pair-wise appearance change detection. Experiments with multiple classical and deep learning-based change detection approaches are performed on our newly collected TAMpering detection dataset for PARcels, called TAMPAR. We evaluate keypoint and change detection separately, as well as in a unified system for tampering detection. Our evaluation shows promising results for keypoint (Keypoint AP 75.76) and tampering detection (81% accuracy, F1-Score 0.83) on real images. Furthermore, a sensitivity analysis for tampering types, lens distortion and viewing angles is presented. Code and dataset are available at https://a-nau.github.io/tampar.

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