Abstract: A method, non-transitory computer readable medium, and device for clustering a network includes obtaining information regarding a network including relationship information for a plurality of nodes of the network. A weight value for each of a plurality of directly connected pairs of the plurality of nodes is determined, wherein the directly connected pair are identified based on the relationship information. At least one topology score is generated for each of the plurality of nodes. A plurality of clusters is generated using the topology scores and one of the plurality of nodes as a seed node for each of the clusters. At least the seed node used to generate at least a subset of the plurality of clusters is output.

Abstract: Systems and methods for multi-dimensional object detection are described. Embodiments disclose receiving image frames, extracting image components in the image frame, identifying line segments in the extracted components, grouping the line segments into groups, based at least in part on one or more similarities between the slope associated with a line segment and the spatial proximity between the line segments, and merging each of the one or more identified line segments in a selected group into a single line segment. Embodiments additionally disclose detecting the position of one or more objects in the image frame by identifying objects in the image frame, producing a second version of the image frame, applying at least one image classifier to the image frame and the second version of the image frame, and identifying coordinates associated with at least one target object. Some embodiments additionally couple lane and object detection with alert generation.

Abstract: A method, non-transitory computer readable medium, and apparatus that extracts at least one key image from one or more images of an object. Outer boundary markers for an identifier of the object in the at least one key image are detected. An identification sequence from the identifier of the object between the outer boundary markers in the at least one key image is recognized. The recognized identification sequence of the object in the at least one key image is provided.

Abstract: A method and system for analyzing on-road traffic density are provided. The method involves allowing a user to select a video image capturing device and coordinates in a video image frame captured by the video image capturing device such that the coordinates form a region of interest (ROI). The ROI is processed to generate a confidence value and a traffic density value. The traffic density value is compared with a first set of threshold values. Based on the comparison, the traffic density values at different instants in a time window are displayed to enable monitoring of the traffic trend.

Abstract: The present invention provides a method and system for recommending an optimal shopping path to a user. A shopping layout of a shopping store is downloaded on a handheld device. A shopping list is received from the user, comprising one or more shopping items. A final shopping list is created by appending one or more additional shopping items to the shopping list. The optimal shopping path representing a shortest path on the shopping layout traversing each shopping item of the final shopping list is generated.

Abstract: A method, non-transitory computer readable medium, and device for clustering a network includes obtaining information regarding a network including relationship information for a plurality of nodes of the network. A weight value for each of a plurality of directly connected pairs of the plurality of nodes is determined, wherein the directly connected pair are identified based on the relationship information. At least one topology score is generated for each of the plurality of nodes. A plurality of clusters is generated using the topology scores and one of the plurality of nodes as a seed node for each of the clusters. At least the seed node used to generate at least a subset of the plurality of clusters is output.

Abstract: Systems and methods for multi-dimensional object detection are described. Embodiments disclose receiving image frames, extracting image components in the image frame, identifying line segments in the extracted components, grouping the line segments into groups, based at least in part on one or more similarities between the slope associated with a line segment and the spatial proximity between the line segments, and merging each of the one or more identified line segments in a selected group into a single line segment. Embodiments additionally disclose detecting the position of one or more objects in the image frame by identifying objects in the image frame, producing a second version of the image frame, applying at least one image classifier to the image frame and the second version of the image frame, and identifying coordinates associated with at least one target object. Some embodiments additionally couple lane and object detection with alert generation.

Abstract: A method and system for analyzing on-road traffic density are provided. The method involves allowing a user to select a video image capturing device and coordinates in a video image frame captured by the video image capturing device such that the coordinates form a region of interest (ROI). The ROI is processed to generate a confidence value and a traffic density value. The traffic density value is compared with a first set of threshold values. Based on the comparison, the traffic density values at different instants in a time window are displayed to enable monitoring of the traffic trend.

Abstract: A method, non-transitory computer readable medium, and apparatus that extracts at least one key image from one or more images of an object. Outer boundary makers for an identifier of the object in the at least one key image are detected. An identification sequence from the identifier of the object between the outer boundary markers in the at least one key image is recognized. The recognized identification sequence of the object in the at least one key image is provided.