Abstract: Disclosed are methods and systems for monitoring and reporting road violations of vehicles sharing roads with responding emergency vehicles. According to an exemplary method video is captured from a forward and/or rear facing camera mounted to an emergency vehicle, and the video is processed to identify any vehicles in violation within a prescribed distance from the emergency vehicle. A license plate id of a vehicle determined to be in violation is identified and communicated to the appropriate authorities.

Abstract: Methods, systems, and processor-readable media for pruning a training dictionary for use in detecting anomalous events from surveillance video. Training samples can be received, which correspond to normal events. A dictionary can then be constructed, which includes two or more classes of normal events from the training samples. Sparse codes are then generated for selected training samples with respect to the dictionary derived from the two or more classes of normal events. The size of the dictionary can then be reduced by removing redundant dictionary columns from the dictionary via analysis of the sparse codes. The dictionary is then optimized to yield a low reconstruction error and a high-interclass discriminability.

Abstract: Methods and systems for improving automated license plate recognition performance. One or more images of a vehicle can be captured via an automated license plate recognition engine. Vehicle class information associated with the vehicle can be obtained using the automated license place recognition engine. Such vehicle class information can be analyzed with respect to the vehicle. Finally, data can be dynamically adjusted with respect to the vehicle based on a per image basis to enhance recognition of the vehicle via the automated license plate recognition engine.

Abstract: Methods and systems for automatically determining the issuing state of a license plate. An image of a license plate acquired by an ALPR engine can be processed via one or more OCR engines such that each OCR engine among the OCR engines is tuned to a particular state. Confidence data output from the OCR engine(s) can be analyzed (among other factors) to estimate the issuing state associated with the license plate. Multiple observations related to the issuing state can be merged to derive an overall conclusion and assign an associated confidence value with respect to the confidence data and determine a likely issuing state associated with the license plate.

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 and systems for reducing the required footprint of SNoW-based classifiers via optimization of classifier features. A compression technique involves two training cycles. The first cycle proceeds normally and the classifier weights from this cycle are used to rank the Successive Mean Quantization Transform (SMQT) features using several criteria. The top N (out of 512 features) are then chosen and the training cycle is repeated using only the top N features. It has been found that OCR accuracy is maintained using only 60 out of 512 features leading to an 88% reduction in RAM utilization at runtime. This coupled with a packing of the weights from doubles to single byte integers added a further 8× reduction in RAM footprint or a reduction of 68× over the baseline SNoW method.

Abstract: A method determines a license plate layout configuration. The method includes generating at least one model representing a license plate layout configuration. The generating includes segmenting training images each defining a license plate to extract characters and logos from the training images. The segmenting includes calculating values corresponding to parameters of the license plate and features of the characters and logos. The segmenting includes estimating a likelihood function specified by the features using the values. The likelihood function measures deviations between an observed plate and the model. The method includes storing a layout structure and the distributions for each of the at least one model. The method includes receiving as input an observed image including a plate region. The method includes segmenting the plate region and determining a license plate layout configuration of the observed plate by comparing the segmented plate region to the at least one model.

Abstract: A method and system for tracking a customer history data utilizing a combination of vehicle and facial information. A license plate image with respect to a customer vehicle can be captured and information with respect to the license plate obtained. The license plate information can be employed as an unique customer identifier with respect to customer history data, if the customer places an order (e.g., drive-through service). Facial images of the customer upon entry to a store and/or upon checkout can be captured and processed. The checkout image can be matched against a set of images in order to associate customer order information with customer history data identified from the license plate information for tracking an in-store order transaction.

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: Methods, systems and processor-readable media for providing a license plate overlay decal with an infrared readable annotation mark for an optical character recognition and segmentation. The annotation mark with respect to character image of a license plate can be designed by training an ALPR engine to improve automatic license plate recognition performance. A plate overlay decal can be rendered with the annotation mark and attached to a license plate. The annotation mark can also be directly placed on the license plate when the license plate is rendered. The annotation mark is visible when illuminated by an infrared light and the license plate appears normal in visible light. The annotation mark enables an ALPR imaging system to obtain more information for each character and utilize the information to improve conclusion accuracy.

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 method and system for achieving accurate segmentation of characters with respect to a license plate image within a tight bounding box image. A vehicle image can be captured by an image capturing unit and processed utilizing an ALPR unit. A vertical projection histogram can be calculated to produce an initial character boundary (cuts) and local statistical information can be employed to split a large cut and insert a missing character. The cut can be classified as a valid and/or a suspect character and the suspect character can be analyzed. The suspect character can be normalized and passed to an OCR module for decoding and generating a confidence quote with every conclusion. The non-character images can be rejected at the OCR level by enforcing a confidence threshold. An adjoining suspect narrow character can be combined and the OCR confidence of the combined character can be assessed.

Abstract: Systems and methods of determining a predicted usable life of components, such as a photoreceptor, associated with an imaging device. The systems and methods include a power source configured to increase an electric field across the photoreceptor. A sensor or array is configured to detect charge deficient spots (CDS) in a charge transport layer (CTL) of the photoreceptor as a result of increasing the electric field. The systems and methods are configured to determine the predicted useable life of the photoreceptor based on the detected CDS. The systems and methods are further configured to output a report of the estimation. The estimation is conducted at fixed or variable intervals throughout the life of the photoreceptor and/or imaging device.

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: Methods and systems for improving automated license plate recognition performance. One or more images of a vehicle can be captured via an automated license plate recognition engine. Vehicle class information associated with the vehicle can be obtained using the automated license place recognition engine. Such vehicle class information can be analyzed with respect to the vehicle. Finally, data can be dynamically adjusted with respect to the vehicle based on a per image basis to enhance recognition of the vehicle via the automated license plate recognition engine.

Abstract: Methods and systems for automatically determining the issuing state of a license plate. An image of a license plate acquired by an ALPR engine can be processed via one or more OCR engines such that each OCR engine among the OCR engines is tuned to a particular state. Confidence data output from the OCR engine(s) can be analyzed (among other factors) to estimate the issuing state associated with the license plate. Multiple observations related to the issuing state can be merged to derive an overall conclusion and assign an associated confidence value with respect to the confidence data and determine a likely issuing state associated with the license plate.

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: Methods, systems and processor-readable media for providing a license plate overlay decal with an infrared readable annotation mark for an optical character recognition and segmentation. The annotation mark with respect to character image of a license plate can be designed by training an ALPR engine to improve automatic license plate recognition performance. A plate overlay decal can be rendered with the annotation mark and attached to a license plate. The annotation mark can also be directly placed on the license plate when the license plate is rendered. The annotation mark is visible when illuminated by an infrared light and the license plate appears normal in visible light. The annotation mark enables an ALPR imaging system to obtain more information for each character and utilize the information to improve conclusion accuracy.

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.

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.