| CPC G06V 10/443 (2022.01) [G06V 10/40 (2022.01); G06V 20/50 (2022.01)] | 10 Claims |
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1. A method for performing re-identification of an object detected in at least one video stream capturing at least one scene, the method comprising:
determining, using a trained Convolutional Neural Network (CNN) algorithm for object detection and for determining feature vectors of detected objects, a first feature vector comprising a set of numerical features for a detected first object in a first image frame capturing one scene of the at least one scene;
determining, using the trained CNN algorithm, a second feature vector comprising a set of numerical features for a detected second object in a second image frame capturing the one scene of the at least one scene;
acquiring a reference feature vector calculated as an average or median feature vector of all or some of multiple feature vectors of a reference model of the one scene of the at least one scene, wherein the reference model is constructed to provide a representation of commonplace traits for the one scene of the at least one scene and is generated using object detections over a predetermined time period for generating an accurate model, and wherein the reference feature vector is pre-constructed prior to entering the determining steps;
assigning a weight to at least one numerical feature of the first feature vector, wherein the weight for a numerical feature of the first feature vector depends on a deviation measure indicative of a degree of deviation of the numerical feature of the first feature vector from a corresponding numerical feature of the acquired reference feature vector of the reference model, wherein numerical features of the first feature vector with larger deviations from the corresponding numerical feature of the reference feature vector are assigned higher weights than numerical features of the first feature vector with smaller deviations from the corresponding numerical feature of the reference feature vector;
assigning a weight to at least one numerical feature of the second feature vector, wherein the weight for a numerical feature of the second feature vector depends on a deviation measure indicative of a degree of deviation of the numerical feature of the second feature vector from a corresponding numerical feature of the reference feature vector of the reference model, wherein numerical features of the second feature vector with larger deviations from the corresponding numerical feature of the reference feature vector are assigned higher weights than numerical features of the second feature vector with smaller deviations from the corresponding numerical feature of the reference feature vector; and
re-identifying the detected second object as being the detected first object when the first feature vector is determined to correspond to the second feature vector according to a similarity measure between the first and second feature vectors being less than a threshold, the similarity measure is calculated using the assigned weights so that numerical features with higher weights are emphasized more than numerical features with lower weights in the calculation of the similarity measure.
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