Abstract: A processor-implemented method and system for detecting objects in a media file using a block based histogram of oriented gradients methodology is described. At least one image representing at least one frame of a video sequence comprising one or more objects of at least one desired object type is received. A region of interest contained in a block of frame is considered. An array containing magnitude and angle values is calculated, normalized for each pixel in the block in the internal memory. The normalized array is aggregated, ported and stored in an external memory. A value or block of values of the aggregated array in the external memory along with the specific feature vector is transferred to the internal memory. Based on a comparison of each value in the aggregated array and the specific feature vector, a desired object type is detected.

Abstract: A processor-implemented method and system for detecting objects in a media file using a block based histogram of oriented gradients methodology is described. At least one image representing at least one frame of a video sequence comprising one or more objects of at least one desired object type is received. A region of interest contained in a block of frame is considered. An array containing magnitude and angle values is calculated, normalized for each pixel in the block in the internal memory. The normalized array is aggregated, ported and stored in an external memory. A value or block of values of the aggregated array in the external memory along with the specific feature vector is transferred to the internal memory. Based on a comparison of each value in the aggregated array and the specific feature vector, a desired object type is detected.