Abstract: Methods and systems for detecting an object part location based on an extended date-driven detection. A specific relevance value between configurations of parts with respect to a set of training images annotated with annotating objects can be defined. A similarity learning algorithm can be applied with respect to the parts to obtain a similarity function based on the similarity between the part configurations. The similarity learning algorithm receives a set of positive pair having similar part configuration and a negative pair having different configuration and returns the similarity function that tends to assign a high score to new positive pairs and a low score to negative pairs. A similarity between a new image and the training images can be measured utilizing the learned similarity function to obtain a neighbor image and a visible and/or non-visible part location with respect to the image can be predicted based on the neighbor image.

Abstract: A privacy-preserving multi-query parking management system and method. An image of a vehicle with respect to a specific part (e.g., license plate) can be captured when the vehicle enters via an entry point. An image signature can be computed by embedding the image into a descriptor space and the image signature can be stored in a database together with relevant metadata. An image of a vehicle with respect to the specific part can be captured when the vehicle exits via at least one exit point and an image signature can be computed. An entry-exit association with respect to the vehicle can be performed by searching for a most similar image signature in the database. Additionally, given a textual query (e.g., a license plate number), a signature can be computed by embedding the text into the same descriptor space.

Abstract: Methods, systems and processor-readable media for vehicle classification. In general, one or more vehicles can be scanned utilizing a laser scanner to compile data indicative of an optical profile of the vehicle(s). The optical profile associated with the vehicle(s) is then pre-processed. Particular features are extracted from the optical profile following pre-processing of the optical profile. The vehicle(s) can be then classified based on the particular features extracted from the optical feature. A segmented laser profile is treated as an image and profile features that integrate the signal in one of the two directions of the image and Fisher vectors which aggregate statistics of local “patches” of the image are computed and utilized as part of the extraction and classification process.

Abstract: A system and method for comparing a text image with or without a wildcard character and a character string are provided. The method includes embedding a character string into a vectorial space by extracting a set of features from the character string and generating a character string representation based on the extracted features, such as a spatial pyramid bag of characters (SPBOC) representation. A text image is embedded into a vectorial space by extracting a set of features from the text image and generating a text image representation based on the text image extracted features. A similarity between the text image representation and the character string representation is computed, which includes computing a function of the text image representation and character string representation.

Abstract: A method includes adapting the universal generative model of local descriptors to a first camera to obtain a first camera-dependent generative model. The same universal generative model is also adapted to a second camera to obtain a second camera-dependent generative model. From a first image captured by the first camera, a first image-level descriptor is extracted, using the first camera-dependent generative model. From a second image captured by the second camera, a second image-level descriptor is extracted using the second camera-dependent generative model. A similarity is computed between the first image-level descriptor and the second image-level descriptor. Information is output, based on the computed similarity. The adaptation allows differences between the image-level descriptors to be shifted towards deviations in image content, rather than the imaging conditions.

Abstract: A system and method for classifying vehicles from laser scan data by receiving laser scan data corresponding to multiple vehicles from a laser scanner; extracting vehicle shapes corresponding to the multiple vehicles based on the laser scan data; aligning the vehicle shapes; and generating vehicle profiles based on the aligned vehicle shapes. The system and method can further include aligning the vehicle shapes using sequence kernels, such as global alignment kernels, and constraining the sequence kernels based on determined weights.

Abstract: A method for recognition of an identifier such as a license plate includes storing first visual signatures, each extracted from a first image of a respective object, such as a vehicle, captured at a first location, and first information associated with the first captured image, such as a time stamp. A second visual signature is extracted from a second image of a second object captured at a second location and second information associated with the second captured image is acquired. A measure of similarity is computed between the second visual signature and at least some of the first visual signatures to identify a matching one. A test is performed, which is a function of the first and the second information associated with the matching signatures. Only when it is confirmed that the test has been met, identifier recognition is performed to identify the identifier of the second object.

Abstract: A system and method for comparing a text image with or without a wildcard character and a character string are provided. The method includes embedding a character string into a vectorial space by extracting a set of features from the character string and generating a character string representation based on the extracted features, such as a spatial pyramid bag of characters (SPBOC) representation. A text image is embedded into a vectorial space by extracting a set of features from the text image and generating a text image representation based on the text image extracted features. A similarity between the text image representation and the character string representation is computed, which includes computing a function of the text image representation and character string representation.

Abstract: A system and method for computing confidence in an output of a text recognition system includes performing character recognition on an input text image with a text recognition system to generate a candidate string of characters. A first representation is generated, based on the candidate string of characters, and a second representation is generated based on the input text image. A confidence in the candidate string of characters is computed based on a computed similarity between the first and second representations in a common embedding space.

Abstract: A method includes adapting the universal generative model of local descriptors to a first camera to obtain a first camera-dependent generative model. The same universal generative model is also adapted to a second camera to obtain a second camera-dependent generative model. From a first image captured by the first camera, a first image-level descriptor is extracted, using the first camera-dependent generative model. From a second image captured by the second camera, a second image-level descriptor is extracted using the second camera-dependent generative model. A similarity is computed between the first image-level descriptor and the second image-level descriptor. Information is output, based on the computed similarity. The adaptation allows differences between the image-level descriptors to be shifted towards deviations in image content, rather than the imaging conditions.

Abstract: A privacy-preserving multi-query parking management system and method. An image of a vehicle with respect to a specific part (e.g., license plate) can be captured when the vehicle enters via an entry point. An image signature can be computed by embedding the image into a descriptor space and the image signature can be stored in a database together with relevant metadata. An image of a vehicle with respect to the specific part can be captured when the vehicle exits via at least one exit point and an image signature can be computed. An entry-exit association with respect to the vehicle can be performed by searching for a most similar image signature in the database. Additionally, given a textual query (e.g., a license plate number), a signature can be computed by embedding the text into the same descriptor space.

Abstract: A system and method for classifying vehicles from laser scan data by receiving laser scan data corresponding to multiple vehicles from a laser scanner; extracting vehicle shapes corresponding to the multiple vehicles based on the laser scan data; aligning the vehicle shapes; and generating vehicle profiles based on the aligned vehicle shapes. The system and method can further include aligning the vehicle shapes using sequence kernels, such as global alignment kernels, and constraining the sequence kernels based on determined weights.

Abstract: A system and method enable generating a specific object detector for a category of interest. The method includes identifying seed objects in frames of a video sequence with a pre-trained generic detector for the category. An appearance model is iteratively learned for each of the seed objects using other frames in which the seed object is identified. The appearance models are learned jointly to optimize a loss function which accounts for the loss of incorrectly labeling sub-images and a regularization term which measures a distance between the appearance models. The loss of incorrectly labeling sub-images is determined using a motion model which predicts the location of the seed object in the subsequent frames so that sub-images outside the location that the current appearance model contribute to the loss. The specific object detector is then generated by aggregating the optimized appearance models.

Abstract: A computer implemented method for localization of an object, such as a license plate, in an input image includes generating a task-dependent representation of the input image based on relevance scores for the object to be localized. The relevance scores are output by a classifier for a plurality of locations in the input image, such as patches. The classifier is trained on patches extracted from training images and their respective relevance labels. One or more similar images are identified from a set of images, based on a comparison of the task-dependent representation of the input image and task-dependent representations of images in the set of images. A location of the object in the input image is identified based on object location annotations for the similar images.

Abstract: Methods and systems for detecting an object part location based on an extended date-driven detection. A specific relevance value between configurations of parts with respect to a set of training images annotated with annotating objects can be defined. A similarity learning algorithm can be applied with respect to the parts to obtain a similarity function based on the similarity between the part configurations. The similarity learning algorithm receives a set of positive pair having similar part configuration and a negative pair having different configuration and returns the similarity function that tends to assign a high score to new positive pair and a low score to negative pairs. A similarity between a new image and the training images can be measured utilizing the learned similarity function to obtain a neighbor image and a visible and/or non-visible part location with respect to the image can be predicted based on the neighbor image.

Abstract: A system and method enable generating a specific object detector for a category of interest. The method includes identifying seed objects in frames of a video sequence with a pre-trained generic detector for the category. An appearance model is iteratively learned for each of the seed objects using other frames in which the seed object is identified. The appearance models are learned jointly to optimize a loss function which accounts for the loss of incorrectly labeling sub-images and a regularization term which measures a distance between the appearance models. The loss of incorrectly labeling sub-images is determined using a motion model which predicts the location of the seed object in the subsequent frames so that sub-images outside the location that the current appearance model contribute to the loss. The specific object detector is then generated by aggregating the optimized appearance models.

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: An instance-level retrieval method and system are provided. A representation of a query image is embedded in a multi-dimensional space using a learned projection. The projection is learned using category-labeled training data to optimize a classification rate on the training data. The joint learning of the projection and the classifiers improves the computation of similarity/distance between images by embedding them in a subspace where the similarity computation outputs more accurate results. An input query image can thus be used to retrieve similar instances in a database by computing the comparison measure in the embedding space.

Abstract: A method for recognition of an identifier such as a license plate includes storing first visual signatures, each extracted from a first image of a respective object, such as a vehicle, captured at a first location, and first information associated with the first captured image, such as a time stamp. A second visual signature is extracted from a second image of a second object captured at a second location and second information associated with the second captured image is acquired. A measure of similarity is computed between the second visual signature and at least some of the first visual signatures to identify a matching one. A test is performed, which is a function of the first and the second information associated with the matching signatures. Only when it is confirmed that the test has been met, identifier recognition is performed to identify the identifier of the second object.

Abstract: A system and method for comparing a text image and a character string are provided. The method includes embedding a character string into a vectorial space by extracting a set of features from the character string and generating a character string representation based on the extracted features, such as a spatial pyramid bag of characters (SPBOC) representation. A text image is embedded into a vectorial space by extracting a set of features from the text image and generating a text image representation based on the text image extracted features. A compatibility between the text image representation and the character string representation is computed, which includes computing a function of the text image representation and character string representation.