Patent application EP19216131A (EP), filed 2019-12-13
Publication number: EP3751480A1
Title: SYSTEM AND METHOD FOR DETECTING ON-STREET PARKING VIOLATIONS
Abstract: This disclosure relates generally to method and system for detecting on-street parking violations. The method include capturing, by using an media capturing device embodied in an electronic device mounted in a vehicle, a video stream of a scene during a trip of the vehicle. The video stream is processed at the electronic device to identify target objects such as no-parking signage and vehicles parked in the vicinity thereof. A meta-information associated with the target objects is stored in form of a short-term historian in a repository associated with the electronic device. The absolute locations of the target objects is determined and the historian is updated with the values of the absolute locations. A set of unique target objects is determined from amongst the target objects and a meta-information associated with the unique objects is sent to a cloud server for determining parking violations.
IPC codes: G06K   9/    32            A I
G06Q  10/    06            A I
G06Q  50/    30            A I
G06Q  50/    26            A I
G06Q  10/   063            A I
G06Q  50/    40            A I
G06V  20/    58            A I
Claims: number: 1
text: A processor implemented method for detecting on-street parking violations, comprising:
number: 2
text: The processor implemented method of claim 1, wherein determining the parking violations comprises sending the meta-information associated with parked vehicles to a cloud server, the cloud server comprising one or more second hardware processors configured to determine the parking violations based on the meta-information, and
wherein the meta-information comprises at least time of detection of the objects of interest, location of the media capturing device at the time of detection, and the distance of the media capturing device from the object of interests.
number: 3
text: The processor implemented method of claim 2, wherein the set of unique objects are identified from amongst the plurality of objects of interest based on a sequential clustering of the absolute locations of the plurality of objects of interest, wherein the sequential clustering of the absolute locations of the plurality of objects of interest comprises:
number: 4
text: The processor implemented method of claim 1, wherein processing the video stream to identify the plurality of objects of interest comprises locating, using convolutional neural network (CNN) models, object detection instances in the video stream, and identifying the object detection instance as one of a parked vehicles and a no-parking signage.
number: 5
text: The processor implemented method of claim 2, further comprises on detection of a no-parking signage:
number: 6
text: The processor implemented method of claim 2, wherein identifying each vehicle from amongst the one or more vehicles as one of parked vehicle and moving vehicle comprises differentiating between parked vehicles and moving vehicles from amongst the plurality of objects of interest, wherein differentiating comprises:
number: 7
text: The processor implemented method of claim 2, wherein computing the absolute locations based on the determination of relative locations values of the media capturing device with respect to one or more vehicles, comprises:
number: 8
text: A system (300), comprising:
number: 9
text: The system of claim 8, wherein to determine the parking violations, the one or more first hardware processors are configured by the instructions to send the meta-information associated with parked vehicles to a cloud server, the cloud server comprises one or more second hardware processors configured to determine the parking violations based on the meta-information, and wherein the meta-information comprises at least time of detection of the objects of interest, location of the media capturing device at the time of detection, and the distance of the media capturing device from the object of interests.
number: 10
text: The system of claim 9, wherein the one or more first hardware processors are configured by the instructions to identify the set of unique objects from amongst the plurality of objects of interest based on a sequential clustering of the absolute locations of the plurality of objects of interest, wherein to perform the sequential clustering of the absolute locations of the plurality of objects of interest, the first hardware processor is configured to:
number: 11
text: The system of claim 9, wherein to process the video stream to identify the plurality of objects of interest, the one or more first hardware processors are configured by the instructions to locate, using convolutional neural network (CNN) models, object detection instances in the video stream, and identifying the object detection instance as one of a parked vehicles and a no-parking signage.
number: 12
text: The system of claim 9, wherein the one or more first hardware processors are by the instructions to perform, on detection of a no-parking signage:
number: 13
text: The system of claim 9, wherein to identifying each vehicle from amongst the one or more vehicles as one of parked vehicle and moving vehicle, the one or more first hardware processor are configured by the instructions to further differentiate between parked vehicles and moving vehicles from amongst the plurality of objects of interest, and wherein to differentiate, the the one or more first hardware processor are configured by the instructions to:
number: 14
text: The system of claim 9, wherein to compute the absolute locations based on the determination of relative locations values of the media capturing device, the one or more first hardware processor are configured by the instructions to:
number: 15
text: One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:
Office action: search_report, dated 2020-12-16
Prior art cited: ref_id: EP3179416A1
ref_type: patent
severity: background
ref_id: CN109147340A
ref_type: patent
severity: background
ref_id: WO2018116189A1
ref_type: patent
severity: background
ref_id: US2019096069A1
ref_type: patent
severity: background
ref_id: US2018240336A1
ref_type: patent
severity: background
ref_id: WO2018116189
ref_type: patent
severity: background
ref_id: US2018240336
ref_type: patent
severity: background
ref_id: CN109147340
ref_type: patent
severity: background
ref_id: US2019096069
ref_type: patent
severity: background
Final disposition: pending (2020-12-16)