US 12,406,503 B2
Method and apparatus for the detection of behaviours in a retail environment
Dan Alin Crisfalusi, Sacalaz jud. Timis (RO); Alan O'Herlihy, Cork (IE); Cristina Todoran, Judetul Arad (RO); Vasile Gui, Timisoara (RO); Dan Pescaru, Timisoara (RO); Ciprian Petru David, Timisoara (RO); Cosmin Cernazanu, Timisoara (RO); and Arion Alexandru, Timisoara (RO)
Assigned to EVERSEEN LIMITED, Blackpool (IE)
Appl. No. 18/280,136
Filed by EVERSEEN LIMITED, Blackpool (IE)
PCT Filed Mar. 3, 2022, PCT No. PCT/EP2022/055491
§ 371(c)(1), (2) Date Sep. 1, 2023,
PCT Pub. No. WO2022/184872, PCT Pub. Date Sep. 9, 2022.
Claims priority of application No. 21160834 (EP), filed on Mar. 4, 2021.
Prior Publication US 2024/0144689 A1, May 2, 2024
Int. Cl. G06V 20/52 (2022.01); G06T 7/20 (2017.01); G06T 7/73 (2017.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 40/20 (2022.01)
CPC G06V 20/52 (2022.01) [G06T 7/20 (2013.01); G06T 7/73 (2017.01); G06V 10/25 (2022.01); G06V 10/764 (2022.01); G06V 40/20 (2022.01); G06T 2207/30241 (2013.01); G06V 2201/07 (2022.01)] 10 Claims
OG exemplary drawing
 
1. A method for identification of suspect behaviour in a retail environment, comprising:
detecting a person in a frame of a stream of video data obtained from a plurality of video sensors in the retail environment, wherein detecting the person comprises establishing localization information for the detected person, by establishing a bounding box framing the person;
classifying the identified person as a tracked person or a non-tracked person;
tracking the path of the tracked person about the retail environment, wherein tracking the path of the tracked person comprises encoding the appearance of the person based on a plurality of semantic features selected from a list including visual appearance, body movement or interaction with the surroundings in the retail environment;
extracting by a behaviour detection unit, a set of activities of the tracked person from the one or more frames of the stream of video data, wherein extracting the set of activities comprises estimating a set of poses of tracked person, and wherein estimating the set of poses comprises identifying a predefined set of points on the tracked person and detecting successive movements of each of the predefined set of points over a time interval, and wherein the behaviour detection unit comprises a trajectory computation module adapted to output a predicted trajectory for the tracked person; an object detection module configured to detect an object which the tracked person picked up in the retail environment and assign a unique object identifier to the object; and a human pose estimation module for the detection of the set of activities or behaviours of the identified tracked person;
assigning a numeric value to each extracted activity in the set of extracted activities, said numeric value being representative of a threat level of the activity;
accumulating said numeric values to provide a behaviour score; and
identifying a behaviour as being suspect when the behaviour score reaches a target threshold value associated with the behaviour.