Abstract: A method, non-transitory computer readable medium and apparatus for performing a person re-identification using an overhead view image are disclosed. For example, the method includes receiving a plurality of overhead view images, detecting a target person in one or more of the plurality of overhead view images, creating a probe image of the target person, receiving a selection of the probe image containing the target person, selecting one or more of the plurality of processed images that has a similar distortion profile as a distortion profile of the probe image based on a radial distance of the target person from a center of a respective overhead view image of the plurality overhead view images used to generate the probe image and performing the person-re-identification of the target person using the one or more of the plurality of processed images that are selected.

Abstract: A method, non-transitory computer readable medium and apparatus for performing a person re-identification using an overhead view image are disclosed. For example, the method includes receiving a plurality of overhead view images, detecting a target person in one or more of the plurality of overhead view images, creating a probe image of the target person, receiving a selection of the probe image containing the target person, selecting one or more of the plurality of processed images that has a similar distortion profile as a distortion profile of the probe image based on a radial distance of the target person from a center of a respective overhead view image of the plurality overhead view images used to generate the probe image and performing the person-re-identification of the target person using the one or more of the plurality of processed images that are selected.

Abstract: A method, non-transitory computer readable medium, and apparatus for directing a vehicle in a side-by-side drive-thru are disclosed. For example, the method receives one or more video images of a side-by-side drive-thru comprising two or more lanes, detects a vehicle approaching an entrance of the side-by-side drive-thru, calculating an estimated order time for the vehicle and directs the vehicle to one of the two or more lanes based on the estimated order time for the vehicle or a previously estimated order time of each one of the a plurality of vehicles already in the first lane and the second lane of the drive-thru.

Abstract: Provided is a method and system of tracking partially occluded objects using an elastic deformation model. According to an exemplary method and system, partially occluded vehicles are detected and tracked in a scene including side-by-side drive-thru lanes.

Abstract: A method, system, and computer-usable tangible storage device for robustly cropping and accurately recognizing license plates to account for noise sources and interfering artifacts are disclosed. License plate images and sub-images can be tightly cropped utilizing an image-based classifier and gradient-based cropping. An image-based classifier can identify the location of valid characters within the image. Because of a number of noise sources, such as, for example, residual plate rotation and shear in the characters within the image, the image-based classifier performs a “rough” identification of the image boundaries. An additional processing step utilizing gradient-based cropping is performed to fine-tune the license plate image boundaries. Gradient-based cropping eliminates unwanted border artifacts that could substantially impact the segmentation and license plate character recognition results.

Abstract: A computer-implemented method, system, and computer-readable medium is disclosed for determining a sequence order for vehicles in one or more image frames from an operational video, the operational video acquired from a fixed video camera comprising a field of view associated with a vehicle merge area where upstream traffic from a first lane and upstream traffic from a second lane merge to a single lane. The method can include obtaining operational video from a fixed video camera; detecting, within a region of interest (ROI) of the one or more image frames from the operational video, a first area and a second area in the vehicle merge area using a trained classifier that is trained to detect the first area and the second area; and determining the sequence order of the vehicles based on the first area and the second area that are detected.

Abstract: A segmentation free method and system for automatic license plate recognition. An OCR classifier can be swept across an image of a license plate. Characters and their locations can be inferred with respect to the image of the license plate using probabilistic inference based on a Hidden Markov Model (HMM). A language model can be combined with a license plate candidate from the HMM to infer the optimal or best license plate code. The language model can be configured by employing a corpus of license plate codes, wherein the corpus includes a distribution representative of training sets and tests sets.

Abstract: Video-based object tracking accuracy is improved by a tentative identification of objects to be tracked. An identified blob that does not encompass a previously established set of tracking features (“tracker”) triggers initialization of an infant tracker. If that tracker remains the only tracker or becomes the “oldest” tracker associated with a blob identified in subsequent video frames, the “age” of the tracker is increased. If, in subsequent frames, the tracker is encompassed by a blob that is associated with an “older” tracker, the “age” of the tracker is decreased. Infant trackers that reach or exceed a threshold “age” are promoted to adult status. Adult trackers can be processed as being associated with valid objects. Trackers established for blobs identified due to mask segmentation tend not to cause false object detections. When segmentation is corrected, blob segments are combined and redundant trackers for the associated object are demoted and ignored.

Abstract: A method, non-transitory computer readable medium, and apparatus for side-by-side traffic location load balancing are disclosed. For example, the method receives one or more video images of a side-by-side traffic location, determines a number of cars in a first lane and a number of cars in a second lane of the side-by-side traffic location based upon the one or more video images, calculates a delta between the number of cars in the first lane and the number of cars in the second lane and recommends the first lane or the second lane based upon the delta and a respective weighting factor associated with the first lane and the second lane to provide a load balance of the cars at the side-by-side traffic location.

Abstract: A method, non-transitory computer readable medium, and apparatus for directing a vehicle in a side-by-side drive-thru are disclosed. For example, the method receives one or more video images of a side-by-side drive-thru comprising two or more lanes, detects a vehicle approaching an entrance of the side-by-side drive-thru, calculating an estimated order time for the vehicle and directs the vehicle to one of the two or more lanes based on the estimated order time for the vehicle or a previously estimated order time of each one of the a plurality of vehicles already in the first lane and the second lane of the drive-thru.

Abstract: Methods and systems for selecting a target vehicle for occupancy detection utilizing vehicle identification information. The vehicle identification information (e.g., license plate information) can be obtained from a vehicle identification unit (e.g., ALPR) to identify a vehicle approaching a high occupancy measurement zone. The vehicle identification information from the vehicle identification unit can be transferred to a vehicle occupancy unit having a flash illuminator unit and an image-capturing unit. The flash illuminator unit and the image-capturing unit are not enabled if the vehicle's occupancy state has already been measured at a prior location based on the vehicle identification information. The vehicle occupancy detection unit and the vehicle identification unit work together in a coordinated fashion to reduce the number of times the flash illuminator unit needs to fire and extends the life of the illuminator unit, thus reducing the cost of maintenance.

Abstract: A system and method for automatically recognizing license plate information, the method comprising receiving an image of a license plate, and generating a plurality of image processing data sets, wherein each image processing data set of the plurality of image processing data sets is associated with a score of a plurality of scores by a scoring process comprising determining one or more image processing parameters, generating the image processing data set by processing the image using the one or more image processing parameters, generating the score based on the image processing data, and associating the image processing data set with the score.

Abstract: Methods, systems, and processor-readable media for data augmentation utilized in an automatic license plate recognition engine. A machine-readable code can be associated with an automatic license plate recognition engine. The machine-readable code can be configured to define parameters that drive processing within the automatic license plate recognition engine to produce recognition results thereof and enhance a machine readability of a license plate recognized and analyzed via the automatic license plate recognition engine.

Abstract: Methods and systems for achieving accurate segmentation of characters with respect to a license plate image utilizing a reinforcement learning approach. A vehicle image can be captured by an image capturing unit and processed utilizing an ALPR (Automatic License Plate Recognition) unit. The reinforcement learning (RL) approach can be configured to initialize a segmentation agent with a starting location. A proper segmentation path (cuts) from top to bottom and from a darker to lighter area in a cropped license plate image can be identified by the segmentation agent during a training phase. Rewards can be provided based on a number of good and bad moves. The association between a current state and a sensory input with a preferred action can be learned by the segmentation agent at the end of the training phase.

Abstract: Methods, systems and processor-readable media for determining, post training, which locations of a classifier window are most significant in discriminating between class and non-class objects. The important locations can be determined by calculating the mean and standard deviation of every pixel location in the classifier context for both the positive and negative samples of the classifier. Using a combination of t-scores and mean differences, the importance of all pixel locations in the classifier score can be rank ordered. A sufficient number of pixel locations can then be selected to achieve a detection rate close enough to the full classifier for a particular application.

Abstract: Provided is a method and system of tracking partially occluded objects using an elastic deformation model. According to an exemplary method and system, partially occluded vehicles are detected and tracked in a scene including side-by-side drive-thru lanes.

Abstract: Video-based object tracking accuracy is improved by a tentative identification of objects to be tracked. An identified blob that does not encompass a previously established set of tracking features (“tracker”) triggers initialization of an infant tracker. If that tracker remains the only tracker or becomes the “oldest” tracker associated with a blob identified in subsequent video frames, the “age” of the tracker is increased. If, in subsequent frames, the tracker is encompassed by a blob that is associated with an “older” tracker, the “age” of the tracker is decreased. Infant trackers that reach or exceed a threshold “age” are promoted to adult status. Adult trackers can be processed as being associated with valid objects. Trackers established for blobs identified due to mask segmentation tend not to cause false object detections. When segmentation is corrected, blob segments are combined and redundant trackers for the associated object are demoted and ignored.

Abstract: A method for processing an image of a scene of interest includes receiving an original target image of a scene of interest at an image processing device from an image source device, the original target image exhibiting shadowing effects associated with the scene of interest when the original target image was captured, the original target image comprising a plurality of elements and representing an instantaneous state for the scene of interest, pre-processing the original target image using a modification identification algorithm to identify elements of the original target image to be modified, and generating a copy mask with a mask region representing the elements to be modified and a non-mask region representing other elements of the original target image. An image processing device for processing an image of a scene of interest and a non-transitory computer-readable medium are also provided.

Abstract: A method for processing an image of a scene of interest includes receiving an original target image of a scene of interest at an image processing device from an image source device, the original target image exhibiting shadowing effects associated with the scene of interest when the original target image was captured, the original target image comprising a plurality of elements and representing an instantaneous state for the scene of interest, pre-processing the original target image using a modification identification algorithm to identify elements of the original target image to be modified, and generating a copy mask with a mask region representing the elements to be modified and a non-mask region representing other elements of the original target image. An image processing device for processing an image of a scene of interest and a non-transitory computer-readable medium are also provided.

Abstract: Methods and systems for character segmentation in an automatic license plate recognition application. One or more images of a license plate are acquired. Then, a pixel-level importance may be calculated with respect to the image(s) of the license plate based on information within the image, such as gradient information and raw grayscale information. A seam selection can be then applied with respect to the pixel-level importance map and the image(s) by enforcing constraints based on known characteristics of license plates in order to provide for character segmentation with respect to the image(s) of the license plate.