Customer Track Matching Using Shopping Receipt Data
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Solution Overview
Problem
Current target tracking technologies in offline sales venues face challenges in accurately associating shopping receipt data with customer tracks due to factors like occlusion, light conditions, and personnel density, leading to incomplete or incorrect tracking of customer movements.
Innovation Solution
A method is introduced to determine a candidate customer track set by counting matching items and track points of interest in a shopping receipt data group, using location and time attributes to identify the correct customer track, which improves the accuracy of target tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If target tracking model is used in offline sales venue, then customer tracks can be obtained, but tracking accuracy deteriorates due to occlusion, light conditions and personnel density
Solution Approach 1:
The patent introduces shopping receipt data as an intermediary element to bridge the gap between fragmented tracklets and complete customer tracks. By using receipt data (containing customer ID, purchase time, purchase location) as a mediator, the system can associate multiple tracklets with the same customer even when visual tracking fails due to occlusion or environmental factors. This intermediary data source compensates for the weaknesses of pure visual tracking in complex retail environments.
Solution Approach 2:
The system implements a feedback mechanism where shopping receipt data is used to verify and correct tracking results. The receipt information (customer ID, time, location) provides feedback that allows the system to identify which tracklets belong to which customer, enabling continuous improvement of tracking accuracy through iterative association and validation of track data against receipt data.
2Area of stationary object
If multiple tracklets are generated for same customer, then tracking coverage is improved, but track continuity deteriorates
Solution Approach 1:
The patent applies merging by combining multiple tracklets that belong to the same customer into a unified track. By using shopping receipt data as the basis for association, the system merges fragmented track segments (identified by different track IDs) into complete customer journeys. This merging process restores track continuity while preserving the comprehensive coverage provided by multiple tracklets.
Solution Approach 2:
The system initially segments customer movement into multiple tracklets for detailed analysis, then uses receipt data to reassemble these segments in the correct sequence. The segmentation allows detailed tracking of customer behavior in different store zones, while the subsequent association based on receipt information restores the complete continuous track.
3Measurement precision
If shopping receipt data is associated with customer track, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The patent makes the shopping receipt data serve multiple functions: it acts as an identifier for customer tracks, provides temporal information for sequencing tracklets, offers spatial information for location-based association, and enables verification of tracking accuracy. This multi-functionality reduces the need for separate systems for each function, thereby limiting the increase in overall system complexity while achieving high data accuracy.
Data Source
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AI summary
The disclosure relates to a method, device and storage medium for processing a target track. According to an embodiment, the method comprises: determining a candidate customer track set of a receipt data group corresponding to one of a plurality of shopping receipts; counting, for each track, the number of sold items matching the track in location in a sold item set indicated by the receipt data group, as a first location matching count of the track; counting, for each track, the number of sold items matching track points of interest in a set of track points of interest of the track in location in the sold item set, as a second location matching count of the track; and determining a customer track corresponding to the receipt data group based on first location matching counts and second location matching counts of a plurality of tracks in the candidate customer track set.