Abstract: The present disclosure discloses device and method for detecting objects placed at multiple ranges from vehicle. Images of the objects may be captured by an image capturing unit housed in the vehicle. The image may be splitted into plurality of sub-images. Further, one or more features may be extracted from the plurality of sub-images. Further, each of the plurality of sub-images may be simultaneously processed for computing gradients associated with the plurality of sub-images. Further, a cell histogram may be created by casting weighted vote for an orientation based histogram channel based on values associated with the gradient. The gradients computed may be normalized by grouping the cells in spatial blocks. Further, a Support vector Machine (SVM) linear classifier may be applied on the plurality of sub-images in order to classify the near object and the far object in a category of a pedestrian or a vehicle.

Abstract: The present application provides a robust, illumination invariant apparatus and method for detecting and recognizing various traffic signs. A robust method for detecting and recognizing the traffic signs using images captured by a digital color and night vision camera, the said method characterized in being illumination invariant comprising the processor implemented steps of: transforming RGB image into HSV color model and subsequently extracting desired color components by using color quantization; filtering the noise components in the HSV color model based on object symmetrical shape property; detecting edges of the objects and subsequently detecting the distinct objects in the noise components filtered image; classifying the shapes of the traffic signs based on shape of the determined distinct objects; and recognizing the classified shapes of the traffic signs by template matching. Further, the method provides the provision for warning the driver by use of the recognized data of the traffic signs.

Abstract: The present disclosure discloses device and method for detecting objects placed at multiple ranges from vehicle. Images of the objects may be captured by an image capturing unit housed in the vehicle. The image may be splitted into plurality of sub-images. Further, one or more features may be extracted from the plurality of sub-images. Further, each of the plurality of sub-images may be simultaneously processed for computing gradients associated with the plurality of sub-images. Further, a cell histogram may be created by casting weighted vote for an orientation based histogram channel based on values associated with the gradient. The gradients computed may be normalized by grouping the cells in spatial blocks. Further, a Support vector Machine (SVM) linear classifier may be applied on the plurality of sub-images in order to classify the near object and the far object in a category of a pedestrian or a vehicle.