METHODS AND SYSTEMS FOR EFFICIENTLY MONITORING PARKING OCCUPANCY
A system and method for determining parking occupancy by constructing a parking area model based on a parking area, receiving image frames from at least one video camera, selecting at least one region of interest from the image frames, performing vehicle detection on the region(s) of interest, determining that there is a change in parking status for a parking space model associated with the region of interest, and updating parking status information for a parking space associated with the parking space model.
1 . A system for determining parking occupancy, the system comprising:
one or more video cameras;
a processing system comprising one or more processors capable of receiving data from the one or more video cameras; and
a memory system comprising one or more computer-readable media, wherein the one or more computer-readable media contain instructions that, when executed by the processing system, cause the processing system to perform operations comprising:
constructing a parking area model based on a parking area, wherein the parking area model comprises one or more parking space models, each associated with a parking space in the parking area;
receiving a set of image frames for the parking area from the one or more video cameras;
selecting a region of interest within an image frame from the set of image frames;
performing vehicle detection on the region of interest within the image frame;
determining that there is a change in parking status for a parking space model associated with the region of interest; and
updating parking status information for a parking space associated with the parking space model based on determining that there is a change in parking status.
2 . The system of claim 1 , wherein the one or more parking space models are three-dimensional volumetric models.
3 . The system of claim 2 , wherein constructing the three-dimensional volumetric models comprises:
receiving a preliminary set of image frames for the parking area from the one or more video cameras;
determining a parking lot layout based on the preliminary set of image frames for the parking area;
estimating parking space volume for the parking spaces models within the parking lot layout based on a viewing angle of the one or more video cameras; and
estimating a probability that a pixel from the preliminary set of image frames belongs to a particular parking space model.
4 . The system of claim 1 , wherein selecting the region of interest within the image frame from the set of image frames comprises selecting the region of interest based on detected motion between the image frame and at least one previous image frame.
5 . The system of claim 4 , wherein the region of interest is selected based on the detected motion overlapping a parking space model from the parking area model.
6 . The system of claim 1 , the operations further comprising tracking points of an object associated with a region of an image frame where motion is detected within the set of image frames.
7 . The system of claim 6 , wherein the region of interest is selected based on at least one of:
a determination that a threshold number of tracking points stopped at a parking space model from the parking area model; and
a determination that a threshold number of tracking points leave a parking space model, from the parking area model, from which they originated.
8 . The system of claim 1 , the operations further comprising monitoring pixel intensities of pixels within each image frame of the set of image frames, wherein:
the region of interest is selected based on a determination that pixel intensities of monitored pixels vary greater than a threshold amount between image frames; and
the monitored pixels are associated with at least one of the one or more parking space models.
9 . The system of claim 1 , the operations further comprising monitoring pixel intensities of pixels within each image frame of the set of image frames, wherein the region of interest is selected based on a determination that, between image frames, pixel intensities change from pixel intensities associated with occupied parking space models to pixel intensities associated with non-occupied parking space models.
10 . The system of claim 6 , the operations further comprising:
monitoring pixel intensities of pixels within each image frame of the set of image frames:
classifying pixels within each image frame as one of vehicle and non-vehicle in a biased probabilistic manner, wherein the classification is:
biased towards vehicle pixels relative to non-vehicle pixels when a determination is made that a threshold number of tracking points stopped at a parking space model from the parking area model; and
biased towards non-vehicle pixels relative to vehicle pixels when a determination is made that a threshold number of tracking points leave a parking space model, from the parking area model, from which they originated.
11 . A method for determining parking occupancy, comprising:
constructing a parking area model based on a parking area, wherein the parking area model comprises one or more parking space models, each associated with a parking space in the parking area;
receiving a set of image frames for the parking area from one or more video cameras;
selecting a region of interest within an image frame from the set of image frames;
performing vehicle detection on the region of interest within the image frame;
determining that there is a change in parking status for a parking space model associated with the region of interest; and
updating parking status information for a parking space associated with the parking space model based on determining that there is a change in parking status.
12 . The method of claim 11 , wherein the one or more parking space models are three-dimensional volumetric models.
13 . The method of claim 12 , wherein constructing the three-dimensional volumetric models comprises:
receiving a preliminary set of image frames for the parking area from the one or more video cameras;
determining a parking lot layout based on the preliminary set of image frames for the parking area;
estimating parking space volume for the parking spaces models within the parking lot layout based on a viewing angle of the one or more video cameras; and
estimating a probability that a pixel from the preliminary set of image frames belongs to a particular parking space model.
14 . The method of claim 11 , wherein selecting the region of interest within the image frame from the set of image frames comprises selecting the region of interest based on detected motion between the image frame and at least one previous image frame.
15 . The method of claim 14 , wherein the region of interest is selected based on the detected motion overlapping a parking space model from the parking area model.
16 . The method of claim 11 , further comprising tracking points of an object associated with a region of an image frame where motion is detected within the set of image frames.
17 . The method of claim 16 , wherein the region of interest is selected based on at least one of:
a determination that a threshold number of tracking points stopped at a parking space model from the parking area model; and
a determination that a threshold number of tracking points leave a parking space model, from the parking area model, from which they originated.
18 . The method of claim 11 , further comprising monitoring pixel intensities of pixels within each image frame of the set of image frames, wherein:
the region of interest is selected based on a determination that pixel intensities of monitored pixels vary greater than a threshold amount between image frames; and
the monitored pixels are associated with at least one of the one or more parking space models.
19 . The method of claim 11 , further comprising monitoring pixel intensities of pixels within each image frame of the set of image frames, wherein the region of interest is selected based on a determination that, between image frames, pixel intensities change from pixel intensities associated with occupied parking space models to pixel intensities associated with non-occupied parking space models.
20 . The method of claim 16 , further comprising:
monitoring pixel intensities of pixels within each image frame of the set of image frames:
classifying pixels within each image frame as one of vehicle and non-vehicle in a biased probabilistic manner, wherein the classification is:
biased towards vehicle pixels relative to non-vehicle pixels when a determination is made that a threshold number of tracking points stopped at a parking space model from the parking area model; and
biased towards non-vehicle pixels relative to vehicle pixels when a determination is made that a threshold number of tracking points leave a parking space model, from the parking area model, from which they originated.