Abstract: A device may receive a set of images for an object recognition operation to identify one or more objects in the set of images after the set of images are combined into a stitched image. The device may combine the set of images into the stitched image. The device may determine reliability scores for the set of images. The reliability scores may be determined based on a difference in images of the set of images, and the reliability scores may predict a quality of the stitched image that includes an image, of the set of images, to which the reliability scores correspond. The device may determine whether a result of the object recognition operation is likely to satisfy a threshold based on the reliability scores. The device may selectively perform the object recognition operation based on whether the result of the object recognition operation is likely to satisfy the threshold.

Abstract: A device may receive a set of images for an object recognition operation to identify one or more objects in the set of images after the set of images are combined into a stitched image. The device may combine the set of images into the stitched image. The device may determine reliability scores for the set of images. The reliability scores may be determined based on a difference in images of the set of images, and the reliability scores may predict a quality of the stitched image that includes an image, of the set of images, to which the reliability scores correspond. The device may determine whether a result of the object recognition operation is likely to satisfy a threshold based on the reliability scores. The device may selectively perform the object recognition operation based on whether the result of the object recognition operation is likely to satisfy the threshold.

Abstract: A system for adaptive learning based human detection for channel input of captured human image signals, the system comprising: a sensor for tracking real-time images of an environment of interest; a feature extraction and classifiers generation processor for extracting a plurality of features and classifying the features associated with time-space descriptors of image comprising background modeling, Histogram of Oriented Gradients (HOG) and Haar like wavelet; a processor configured to process extracted feature classifiers associated with plurality of real-time images; combine the plurality of feature classifiers of time-space descriptors; evaluate a linear probability of human detection based on a predetermined threshold value of the feature classifiers in a time window having at least one image frame; a counter for counting the number of humans in the real-time images; and a transmission device configured to send the final human detection decision and number thereof to a storage device.

Abstract: A computer-based system and method to improve the multimodal fusion output at the decision level is disclosed. The method proposes computation of a confidence weighted measure for the individual score values obtained for each modality and fuse these new updated scores to get the final decision. These confidence weights are the performance parameters (measured in terms of F-measure) during the offline training step. The process significantly increases the accuracy of the multimodal system.

Abstract: A computer-based system and method to improve the multimodal fusion output at the decision level is disclosed. The method proposes computation of a confidence weighted measure for the individual score values obtained for each modality and fuse these new updated scores to get the final decision. These confidence weights are the performance parameters (measured in terms of F-measure) during the offline training step. The process significantly increases the accuracy of the multimodal system.

Abstract: A system for adaptive learning based human detection for channel input of captured human image signals, the system comprising: a sensor for tracking real-time images of an environment of interest; a feature extraction and classifiers generation processor for extracting a plurality of features and classifying the features associated with time-space descriptors of image comprising background modeling, Histogram of Oriented Gradients (HOG) and Haar like wavelet; a processor configured to process extracted feature classifiers associated with plurality of real-time images; combine the plurality of feature classifiers of time-space descriptors; evaluate a linear probability of human detection based on a predetermined threshold value of the feature classifiers in a time window having at least one image frame; a counter for counting the number of humans in the real-time images; and a transmission device configured to send the final human detection decision and number thereof to a storage device.