Abstract: Systems and methods for forecasting trajectories of objects. The method includes obtaining a prediction model trained to predict future trajectories of objects. The prediction model is trained over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory. The method also include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data.

Abstract: Systems and methods for forecasting trajectories of objects. The method includes obtaining a prediction model trained to predict future trajectories of objects. The prediction model is trained over a first prediction horizon selected to encode inertial constraints in a predicted trajectory and over a second prediction horizon selected to encode behavioral constraints in the predicted trajectory. The method also include generating a planned trajectory of an autonomous vehicle by receiving state data corresponding to the autonomous vehicle, receiving perception data corresponding to an object, predicting a future trajectory of the object based on the perception data and the prediction model, and generating the planned trajectory of the autonomous vehicle based on the future trajectory of the object and the state data.

Abstract: A method and a system for forecasting trajectories in an autonomous vehicle using recurrent neural networks. The method includes receiving a first set of data that comprises time series information corresponding to states of a plurality of objects, analyzing the first set of data to determine a plurality of object trajectory sequences corresponding to the plurality of objects, and using one or more of the plurality of object trajectory sequences as input to train a prediction model for predicting future trajectories of the plurality of objects. The predication model can be trained by defining a first prediction horizon, training the prediction model over the first prediction horizon to generate a semi-trained prediction model, defining a second prediction horizon that is longer than the first prediction horizon, and training the semi-trained prediction model to generate a trained prediction model.

Abstract: Techniques for object re-identification based on temporal context. Embodiments extract, from a first image corresponding to a first camera device and a second image corresponding to a second camera device, a first plurality of patch descriptors and a second plurality of patch descriptors, respectively. A measure of visual similarity between the first image and the second image is computed, based on the first plurality of patch descriptors and the second plurality of patch descriptors. A temporal cost between the first image and the second image is computed, based on a first timestamp at which the first image was captured and a second timestamp at which the second image was captured. The measure of visual similarity and the temporal cost are combined into a single cost function, and embodiments determine whether the first image and the second image depict a common object, using the single cost function.

Abstract: A method and a system for forecasting trajectories in an autonomous vehicle using recurrent neural networks. The method includes receiving a first set of data that comprises time series information corresponding to states of a plurality of objects, analyzing the first set of data to determine a plurality of object trajectory sequences corresponding to the plurality of objects, and using one or more of the plurality of object trajectory sequences as input to train a prediction model for predicting future trajectories of the plurality of objects. The predication model can be trained by defining a first prediction horizon, training the prediction model over the first prediction horizon to generate a semi-trained prediction model, defining a second prediction horizon that is longer than the first prediction horizon, and training the semi-trained prediction model to generate a trained prediction model.

Abstract: Techniques for detecting objects across images captured by camera devices. Embodiments capture, using first and second camera devices, first and second pluralities of images, respectively. First and second reference images are captured using the first and second camera devices. Color descriptors are extracted from the first plurality of images and the second plurality of images, and texture descriptors are extracted from the first plurality of images and the second plurality of images. Embodiments model a first color subspace and a second color subspace for the first camera device and the second camera device, respectively, based on the first and second pluralities of images and the first and second reference images. A data model for identifying objects appearing in images captured using the first and second camera devices is generated, based on the extracted color descriptors, texture descriptors and the first and second color subspaces.

Abstract: There is provided a system including a memory and a processor configured to receive a first image depicting a first object and a second image depicting a second object, divide the first image into a first plurality of patches and the second image into a second plurality of patches, extract plurality of feature vectors from each of the first plurality of patches and a second plurality of feature vectors from the second plurality of patches, determine dissimilarities based on a plurality of patch metrics, each patch dissimilarity measure being a dissimilarity between corresponding patches of the first plurality of patches and the second plurality of patches, compute an image dissimilarity between the first image and the second image based on an aggregate of the plurality of patch dissimilarity measures, evaluate the image dissimilarity to determine a probability of whether the first object and the second object are the same.

Abstract: Techniques for object re-identification based on temporal context. Embodiments extract, from a first image corresponding to a first camera device and a second image corresponding to a second camera device, a first plurality of patch descriptors and a second plurality of patch descriptors, respectively. A measure of visual similarity between the first image and the second image is computed, based on the first plurality of patch descriptors and the second plurality of patch descriptors. A temporal cost between the first image and the second image is computed, based on a first timestamp at which the first image was captured and a second timestamp at which the second image was captured. The measure of visual similarity and the temporal cost are combined into a single cost function, and embodiments determine whether the first image and the second image depict a common object, using the single cost function.

Abstract: Techniques for detecting objects across images captured by camera devices. Embodiments capture, using first and second camera devices, first and second pluralities of images, respectively. First and second reference images are captured using the first and second camera devices. Color descriptors are extracted from the first plurality of images and the second plurality of images, and texture descriptors are extracted from the first plurality of images and the second plurality of images. Embodiments model a first color subspace and a second color subspace for the first camera device and the second camera device, respectively, based on the first and second pluralities of images and the first and second reference images. A data model for identifying objects appearing in images captured using the first and second camera devices is generated, based on the extracted color descriptors, texture descriptors and the first and second color subspaces.

Abstract: There is provided a system including a memory and a processor configured to receive a first image depicting a first object and a second image depicting a second object, divide the first image into a first plurality of patches and the second image into a second plurality of patches, extract plurality of feature vectors from each of the first plurality of patches and a second plurality of feature vectors from the second plurality of patches, determine dissimilarities based on a plurality of patch metrics, each patch dissimilarity measure being a dissimilarity between corresponding patches of the first plurality of patches and the second plurality of patches, compute an image dissimilarity between the first image and the second image based on an aggregate of the plurality of patch dissimilarity measures, evaluate the image dissimilarity to determine a probability of whether the first object and the second object are the same.