Abstract: Calibration and distance prediction for driving assistance is provided. A camera of a vehicle is calibrated to obtain a distance data set. The distance data set includes a distance of each row of pixels of a first image captured by the camera. The distance data set may be further utilized in real-time to predict a distance of an object from the vehicle. Based on the predicted distance, a warning message for an impending collision may be generated and communicated to a driver of the vehicle, thereby facilitating driving assistance to the driver in the real-time.

Abstract: Lane prediction for driving assistance is provided. A camera of a vehicle is calibrated to obtain column values for each row of pixels of an image based on a plurality of lines captured in the image. The plurality of lines may be either parallel or perpendicular to a calibration lane captured in the image. The column values of each row of pixels may be utilized to predict a driving lane for the vehicle. When an on-road object is detected in the predicted driving lane, a warning message for an impending collision may be generated and communicated to a driver of the vehicle, thereby facilitating driving assistance to the driver in real-time.

Abstract: Lane prediction for driving assistance is provided. A camera of a vehicle is calibrated to obtain column values for each row of pixels of an image based on a plurality of lines captured in the image. The plurality of lines may be either parallel or perpendicular to a calibration lane captured in the image. The column values of each row of pixels may be utilized to predict a driving lane for the vehicle. When an on-road object is detected in the predicted driving lane, a warning message for an impending collision may be generated and communicated to a driver of the vehicle, thereby facilitating driving assistance to the driver in real-time.

Abstract: Calibration and distance prediction for driving assistance is provided. A camera of a vehicle is calibrated to obtain a distance data set. The distance data set includes a distance of each row of pixels of a first image captured by the camera. The distance data set may be further utilized in real-time to predict a distance of an object from the vehicle. Based on the predicted distance, a warning message for an impending collision may be generated and communicated to a driver of the vehicle, thereby facilitating driving assistance to the driver in the real-time.

Abstract: Calibration and distance prediction for driving assistance is provided. A camera of a vehicle is calibrated to obtain a distance data set. The distance data set includes a distance of each row of pixels of a first image captured by the camera. The distance data set may be further utilized in real-time to predict a distance of an object from the vehicle. Based on the predicted distance, a warning message for an impending collision may be generated and communicated to a driver of the vehicle, thereby facilitating driving assistance to the driver in the real-time.

Abstract: Disclosed is a method and system for dynamically generating a playlist of multimedia files for a user based upon a mood of the user. The system comprises a mood detection module, a tag allocation module, and a playlist generation module. The mood detection module is configured to determine the mood of the user based upon the one or more emotion tags allocated to records of the user. The tag allocation module is configured to categorize the at least one multimedia file under one or more emotion tags based upon the weights assigned to the one or more emotion tags. The playlist generation module is configured to dynamically generate the playlist of multimedia files based upon the mood of the user and the categorization of the at least one multimedia file under one or more emotion tags.

Abstract: Disclosed is a method and system for dynamically generating a playlist of multimedia files for a user based upon a mood of the user. The system comprises a mood detection module, a tag allocation module, and a playlist generation module. The mood detection module is configured to determine the mood of the user based upon the one or more emotion tags allocated to records of the user. The tag allocation module is configured to categorize the at least one multimedia file under one or more emotion tags based upon the weights assigned to the one or more emotion tags. The playlist generation module is configured to dynamically generate the playlist of multimedia files based upon the mood of the user and the categorization of the at least one multimedia file under one or more emotion tags.